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genetics</title> <meta name="description" content="Search results for: evolutionary genetics"> <meta name="keywords" content="evolutionary genetics"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="evolutionary genetics"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 498</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: evolutionary genetics</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">498</span> Theoretical Approaches to Graphic and Formal Generation from Evolutionary Genetics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luz%20Estrada">Luz Estrada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The currents of evolutionary materialistic thought have argued that knowledge about an object is not obtained through the abstractive method. That is, the object cannot come to be understood if founded upon itself, nor does it take place by the encounter between form and matter. According to this affirmation, the research presented here identified as a problematic situation the absence of comprehension of the formal creation as a generative operation. This has been referred to as a recurrent lack in the production of objects and corresponds to the need to conceive the configurative process from the reality of its genesis. In this case, it is of interest to explore ways of creation that consider the object as if it were a living organism, as well as responding to the object鈥檚 experience as embodied in the designer since it unfolds its genesis simultaneously to the ways of existence of those who are involved in the generative experience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architecture" title="architecture">architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20graphics" title=" theoretical graphics"> theoretical graphics</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetics" title=" evolutionary genetics"> evolutionary genetics</a>, <a href="https://publications.waset.org/abstracts/search?q=formal%20perception" title=" formal perception"> formal perception</a> </p> <a href="https://publications.waset.org/abstracts/158586/theoretical-approaches-to-graphic-and-formal-generation-from-evolutionary-genetics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158586.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">117</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">497</span> Examining the Performance of Three Multiobjective Evolutionary Algorithms Based on Benchmarking Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Metaxiotis">Konstantinos Metaxiotis</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Liagkouras"> Konstantinos Liagkouras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this study is to examine the performance of three well-known multiobjective evolutionary algorithms for solving optimization problems. The first algorithm is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the second one is the Strength Pareto Evolutionary Algorithm 2 (SPEA-2), and the third one is the Multiobjective Evolutionary Algorithms based on decomposition (MOEA/D). The examined multiobjective algorithms are analyzed and tested on the ZDT set of test functions by three performance metrics. The results indicate that the NSGA-II performs better than the other two algorithms based on three performance metrics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MOEAs" title="MOEAs">MOEAs</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=ZDT%20test%20functions" title=" ZDT test functions"> ZDT test functions</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/65331/examining-the-performance-of-three-multiobjective-evolutionary-algorithms-based-on-benchmarking-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65331.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">470</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">496</span> Variational Evolutionary Splines for Solving a Model of Temporomandibular Disorders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alberto%20Hananel">Alberto Hananel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this work is to modelize the occlusion of a person with temporomandibular disorders as an evolutionary equation and approach its solution by the construction and characterizing of discrete variational splines. To formulate the problem, certain boundary conditions have been considered. After showing the existence and the uniqueness of the solution of such a problem, a convergence result of a discrete variational evolutionary spline is shown. A stress analysis of the occlusion of a human jaw with temporomandibular disorders by finite elements is carried out in FreeFem++ in order to prove the validity of the presented method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximation" title="approximation">approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20PDE" title=" evolutionary PDE"> evolutionary PDE</a>, <a href="https://publications.waset.org/abstracts/search?q=Finite%20Element%20Method" title=" Finite Element Method"> Finite Element Method</a>, <a href="https://publications.waset.org/abstracts/search?q=temporomandibular%20disorders" title=" temporomandibular disorders"> temporomandibular disorders</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20spline" title=" variational spline"> variational spline</a> </p> <a href="https://publications.waset.org/abstracts/51438/variational-evolutionary-splines-for-solving-a-model-of-temporomandibular-disorders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51438.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">495</span> A Review on Applications of Evolutionary Algorithms to Reservoir Operation for Hydropower Production</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nkechi%20Neboh">Nkechi Neboh</a>, <a href="https://publications.waset.org/abstracts/search?q=Josiah%20Adeyemo"> Josiah Adeyemo</a>, <a href="https://publications.waset.org/abstracts/search?q=Abimbola%20Enitan"> Abimbola Enitan</a>, <a href="https://publications.waset.org/abstracts/search?q=Oludayo%20Olugbara"> Oludayo Olugbara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms are techniques extensively used in the planning and management of water resources and systems. It is useful in finding optimal solutions to water resources problems considering the complexities involved in the analysis. River basin management is an essential area that involves the management of upstream, river inflow and outflow including downstream aspects of a reservoir. Water as a scarce resource is needed by human and the environment for survival and its management involve a lot of complexities. Management of this scarce resource is necessary for proper distribution to competing users in a river basin. This presents a lot of complexities involving many constraints and conflicting objectives. Evolutionary algorithms are very useful in solving this kind of complex problems with ease. Evolutionary algorithms are easy to use, fast and robust with many other advantages. Many applications of evolutionary algorithms, which are population based search algorithm, are discussed. Different methodologies involved in the modeling and simulation of water management problems in river basins are explained. It was found from this work that different evolutionary algorithms are suitable for different problems. Therefore, appropriate algorithms are suggested for different methodologies and applications based on results of previous studies reviewed. It is concluded that evolutionary algorithms, with wide applications in water resources management, are viable and easy algorithms for most of the applications. The results suggested that evolutionary algorithms, applied in the right application areas, can suggest superior solutions for river basin management especially in reservoir operations, irrigation planning and management, stream flow forecasting and real-time applications. The future directions in this work are suggested. This study will assist decision makers and stakeholders on the best evolutionary algorithm to use in varied optimization issues in water resources management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithm" title="evolutionary algorithm">evolutionary algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective" title=" multi-objective"> multi-objective</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20operation" title=" reservoir operation"> reservoir operation</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20basin%20management" title=" river basin management"> river basin management</a> </p> <a href="https://publications.waset.org/abstracts/34049/a-review-on-applications-of-evolutionary-algorithms-to-reservoir-operation-for-hydropower-production" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">491</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">494</span> A Novel Guided Search Based Multi-Objective Evolutionary Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Baviskar">A. Baviskar</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Sandeep"> C. Sandeep</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Shankar"> K. Shankar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solving Multi-objective Optimization Problems requires faster convergence and better spread. Though existing Evolutionary Algorithms (EA's) are able to achieve this, the computation effort can further be reduced by hybridizing them with innovative strategies. This study is focuses on converging to the pareto front faster while adapting the advantages of Strength Pareto Evolutionary Algorithm-II (SPEA-II) for a better spread. Two different approaches based on optimizing the objective functions independently are implemented. In the first method, the decision variables corresponding to the optima of individual objective functions are strategically used to guide the search towards the pareto front. In the second method, boundary points of the pareto front are calculated and their decision variables are seeded to the initial population. Both the methods are applied to different constrained and unconstrained multi-objective test functions. It is observed that proposed guided search based algorithm gives better convergence and diversity than several well-known existing algorithms (such as NSGA-II and SPEA-II) in considerably less number of iterations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20points" title="boundary points">boundary points</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms%20%28EA%27s%29" title=" evolutionary algorithms (EA's)"> evolutionary algorithms (EA's)</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20search" title=" guided search"> guided search</a>, <a href="https://publications.waset.org/abstracts/search?q=strength%20pareto%20evolutionary%20algorithm-II%20%28SPEA-II%29" title=" strength pareto evolutionary algorithm-II (SPEA-II)"> strength pareto evolutionary algorithm-II (SPEA-II)</a> </p> <a href="https://publications.waset.org/abstracts/40983/a-novel-guided-search-based-multi-objective-evolutionary-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40983.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">493</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">536</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">492</span> Improve Closed Loop Performance and Control Signal Using Evolutionary Algorithms Based PID Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Shahbazian">Mehdi Shahbazian</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Aarabi"> Alireza Aarabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Hadiyan"> Mohsen Hadiyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithm" title="evolutionary algorithm">evolutionary algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20controller" title=" PID controller"> PID controller</a> </p> <a href="https://publications.waset.org/abstracts/24261/improve-closed-loop-performance-and-control-signal-using-evolutionary-algorithms-based-pid-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24261.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">484</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">491</span> Markowitz and Implementation of a Multi-Objective Evolutionary Technique Applied to the Colombia Stock Exchange (2009-2015)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feijoo%20E.%20Colomine%20Duran">Feijoo E. Colomine Duran</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20E.%20Pe%C3%B1aloza%20Corredor"> Carlos E. Pe帽aloza Corredor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There modeling component selection financial investment (Portfolio) a variety of problems that can be addressed with optimization techniques under evolutionary schemes. For his feature, the problem of selection of investment components of a dichotomous relationship between two elements that are opposed: The Portfolio Performance and Risk presented by choosing it. This relationship was modeled by Markowitz through a media problem (Performance) - variance (risk), ie must Maximize Performance and Minimize Risk. This research included the study and implementation of multi-objective evolutionary techniques to solve these problems, taking as experimental framework financial market equities Colombia Stock Exchange between 2009-2015. Comparisons three multiobjective evolutionary algorithms, namely the Nondominated Sorting Genetic Algorithm II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Indicator-Based Selection in Multiobjective Search (IBEA) were performed using two measures well known performance: The Hypervolume indicator and R_2 indicator, also it became a nonparametric statistical analysis and the Wilcoxon rank-sum test. The comparative analysis also includes an evaluation of the financial efficiency of the investment portfolio chosen by the implementation of various algorithms through the Sharpe ratio. It is shown that the portfolio provided by the implementation of the algorithms mentioned above is very well located between the different stock indices provided by the Colombia Stock Exchange. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio" title=" portfolio"> portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz" title=" Markowitz"> Markowitz</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/56680/markowitz-and-implementation-of-a-multi-objective-evolutionary-technique-applied-to-the-colombia-stock-exchange-2009-2015" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">490</span> Combining an Optimized Closed Principal Curve-Based Method and Evolutionary Neural Network for Ultrasound Prostate Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tao%20Peng">Tao Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhao"> Jing Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanqing%20Xu"> Yanqing Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Cai"> Jing Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20prostate%20segmentation" title="ultrasound prostate segmentation">ultrasound prostate segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20closed%20polygonal%20segment%20method" title=" optimized closed polygonal segment method"> optimized closed polygonal segment method</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20neural%20network" title=" evolutionary neural network"> evolutionary neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=smooth%20mathematical%20model" title=" smooth mathematical model"> smooth mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20curve" title=" principal curve"> principal curve</a> </p> <a href="https://publications.waset.org/abstracts/143203/combining-an-optimized-closed-principal-curve-based-method-and-evolutionary-neural-network-for-ultrasound-prostate-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">489</span> Multi-Objective Evolutionary Computation Based Feature Selection Applied to Behaviour Assessment of Children</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Jim%C3%A9nez">F. Jim茅nez</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20J%C3%B3dar"> R. J贸dar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mart%C3%ADn"> M. Mart铆n</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S%C3%A1nchez"> G. S谩nchez</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Sciavicco"> G. Sciavicco</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title="evolutionary computation">evolutionary computation</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a> </p> <a href="https://publications.waset.org/abstracts/44594/multi-objective-evolutionary-computation-based-feature-selection-applied-to-behaviour-assessment-of-children" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44594.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">371</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">488</span> Tractography Analysis of the Evolutionary Origin of Schizophrenia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asmaa%20Tahiri">Asmaa Tahiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mouktafi%20Amine"> Mouktafi Amine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A substantial number of traditional medical research has been put forward to managing and treating mental disorders. At the present time, to our best knowledge, it is believed that fundamental understanding of the underlying causes of the majority psychological disorders needs to be explored further to inform early diagnosis, managing symptoms and treatment. The emerging field of evolutionary psychology is a promising prospect to address the origin of mental disorders, potentially leading to more effective treatments. Schizophrenia as a topical mental disorder has been linked to the evolutionary adaptation of the human brain represented in the brain connectivity and asymmetry directly linked to humans higher brain cognition in contrast to other primates being our direct living representation of the structure and connectivity of our earliest common African ancestors. As proposed in the evolutionary psychology scientific literature the pathophysiology of schizophrenia is expressed and directly linked to altered connectivity between the Hippocampal Formation (HF) and Dorsolateral Prefrontal Cortex (DLPFC). This research paper presents the results of the use of tractography analysis using multiple open access Diffusion Weighted Imaging (DWI) datasets of healthy subjects, schizophrenia-affected subjects and primates to illustrate the relevance of the aforementioned brain regions connectivity and the underlying evolutionary changes in the human brain. Deterministic fiber tracking and streamline analysis were used to generate connectivity matrices from the DWI datasets overlaid to compute distances and highlight disconnectivity patterns in conjunction with other fiber tracking metrics; Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tractography" title="tractography">tractography</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20psychology" title=" evolutionary psychology"> evolutionary psychology</a>, <a href="https://publications.waset.org/abstracts/search?q=schizophrenia" title=" schizophrenia"> schizophrenia</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20connectivity" title=" brain connectivity"> brain connectivity</a> </p> <a href="https://publications.waset.org/abstracts/179350/tractography-analysis-of-the-evolutionary-origin-of-schizophrenia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179350.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">487</span> Evolutionary Methods in Cryptography </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Slaibi%20Alsharafat">Wafa Slaibi Alsharafat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithms (GA) are random algorithms as random numbers that are generated during the operation of the algorithm determine what happens. This means that if GA is applied twice to optimize exactly the same problem it might produces two different answers. In this project, we propose an evolutionary algorithm and Genetic Algorithm (GA) to be implemented in symmetric encryption and decryption. Here, user's message and user secret information (key) which represent plain text to be transferred into cipher text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GA" title="GA">GA</a>, <a href="https://publications.waset.org/abstracts/search?q=encryption" title=" encryption"> encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=decryption" title=" decryption"> decryption</a>, <a href="https://publications.waset.org/abstracts/search?q=crossover" title=" crossover"> crossover</a> </p> <a href="https://publications.waset.org/abstracts/21507/evolutionary-methods-in-cryptography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21507.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">446</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">486</span> Understanding Evolutionary Algorithms through Interactive Graphical Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Javier%20Barrachina">Javier Barrachina</a>, <a href="https://publications.waset.org/abstracts/search?q=Piedad%20Garrido"> Piedad Garrido</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Fogue"> Manuel Fogue</a>, <a href="https://publications.waset.org/abstracts/search?q=Julio%20A.%20Sanguesa"> Julio A. Sanguesa</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20J.%20Martinez"> Francisco J. Martinez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=education" title="education">education</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=evolution%20strategies" title=" evolution strategies"> evolution strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20learning%20applications" title=" interactive learning applications"> interactive learning applications</a> </p> <a href="https://publications.waset.org/abstracts/38035/understanding-evolutionary-algorithms-through-interactive-graphical-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38035.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">485</span> Polycystic Ovarian Syndrome (PCOS) as an Evolutionary Mismatch Disorder: An Argument for the Significance of Hyperandrogenism on Reproductive Fitness in Ancestral Populations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Courtney%20Manthey-Pierce">Courtney Manthey-Pierce</a>, <a href="https://publications.waset.org/abstracts/search?q=Anna%20Warrener"> Anna Warrener</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Polycystic ovarian syndrome (PCOS) is the most common endocrine disruptive disorder in females. PCOS is primarily characterized by polycystic ovaries, anovulation, hirsutism, insulin resistance, and hyperandrogenism. Despite negative reproductive consequences for females from anovulation and endocrine dysfunction, genes associated with the pathogenesis of PCOS are highly hereditable (h2 = 0.72). An evolutionary mismatch occurs when a trait that evolved in one environment has become maladaptive in another environment. The idea that PCOS is an evolutionary mismatch disease has been promoted by several researchers. Each trait of the resulting PCOS phenotype should be investigated individually in order to demonstrate an evolutionary mismatch. Hyperandrogenism is often regarded as the main characteristic of PCOS Hyperandrogenism may have aided with conception in older females, increased bone mineral density, and supported prolonged breastfeeding in nutritionally distressed populations. Because of the high prevalence of PCOS in the modern world, approximately 6%, it is often argued that PCOS emerged in an ancestral population prior to the migration out of Africa approximately 200,000 years ago. This environment would be characterized by sporadic periods of nutrition deficit and resource hardships as the climate began changing. Presently, modern society is characterized by obesity and sedentary lifestyles. The prevalence of obesity renders hyperandrogenism PCOS useless as there are no periods of nutritional distress requiring androgens for increased reproductive rates. In an ancestral environment, hyperandrogenism would likely lead to sporadic anovulation and mild secondary symptoms, however high levels of androgens in a modern environment led to prolonged if not permanent infertility and excessive secondary problems. Thus, hyperandrogenism related to PCOS appears to meet evolutionary mismatch criteria. Seen in this light, PCOS may be effectively treated as a probably evolutionary mismatch. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20mismatch" title="evolutionary mismatch">evolutionary mismatch</a>, <a href="https://publications.waset.org/abstracts/search?q=heritability" title=" heritability"> heritability</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperandrogenism" title=" hyperandrogenism"> hyperandrogenism</a>, <a href="https://publications.waset.org/abstracts/search?q=mismatch%20disorder" title=" mismatch disorder"> mismatch disorder</a> </p> <a href="https://publications.waset.org/abstracts/138984/polycystic-ovarian-syndrome-pcos-as-an-evolutionary-mismatch-disorder-an-argument-for-the-significance-of-hyperandrogenism-on-reproductive-fitness-in-ancestral-populations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138984.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">484</span> Analysis and Simulation of TM Fields in Waveguides with Arbitrary Cross-Section Shapes by Means of Evolutionary Equations of Time-Domain Electromagnetic Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C3%96mer%20Akta%C5%9F">脰mer Akta艧</a>, <a href="https://publications.waset.org/abstracts/search?q=Olga%20A.%20Suvorova"> Olga A. Suvorova</a>, <a href="https://publications.waset.org/abstracts/search?q=Oleg%20Tretyakov"> Oleg Tretyakov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The boundary value problem on non-canonical and arbitrary shaped contour is solved with a numerically effective method called Analytical Regularization Method (ARM) to calculate propagation parameters. As a result of regularization, the equation of first kind is reduced to the infinite system of the linear algebraic equations of the second kind in the space of L2. This equation can be solved numerically for desired accuracy by using truncation method. The parameters as cut-off wavenumber and cut-off frequency are used in waveguide evolutionary equations of electromagnetic theory in time-domain to illustrate the real-valued TM fields with lossy and lossless media. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20regularization%20method" title="analytical regularization method">analytical regularization method</a>, <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20theory%20evolutionary%20equations%20of%20time-domain" title=" electromagnetic theory evolutionary equations of time-domain"> electromagnetic theory evolutionary equations of time-domain</a>, <a href="https://publications.waset.org/abstracts/search?q=TM%20Field" title=" TM Field"> TM Field</a> </p> <a href="https://publications.waset.org/abstracts/44904/analysis-and-simulation-of-tm-fields-in-waveguides-with-arbitrary-cross-section-shapes-by-means-of-evolutionary-equations-of-time-domain-electromagnetic-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44904.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">501</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">483</span> Tractography Analysis and the Evolutionary Origin of Schizophrenia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouktafi%20Amine">Mouktafi Amine</a>, <a href="https://publications.waset.org/abstracts/search?q=Tahiri%20Asmaa"> Tahiri Asmaa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A substantial number of traditional medical research has been put forward to managing and treating mental disorders. At the present time, to our best knowledge, it is believed that a fundamental understanding of the underlying causes of the majority of psychological disorders needs to be explored further to inform early diagnosis, managing symptoms and treatment. The emerging field of evolutionary psychology is a promising prospect to address the origin of mental disorders, potentially leading to more effective treatments. Schizophrenia as a topical mental disorder has been linked to the evolutionary adaptation of the human brain represented in the brain connectivity and asymmetry directly linked to humans' higher brain cognition in contrast to other primates being our direct living representation of the structure and connectivity of our earliest common African ancestors. As proposed in the evolutionary psychology scientific literature, the pathophysiology of schizophrenia is expressed and directly linked to altered connectivity between the Hippocampal Formation (HF) and Dorsolateral Prefrontal Cortex (DLPFC). This research paper presents the results of the use of tractography analysis using multiple open access Diffusion Weighted Imaging (DWI) datasets of healthy subjects, schizophrenia-affected subjects and primates to illustrate the relevance of the aforementioned brain regions' connectivity and the underlying evolutionary changes in the human brain. Deterministic fiber tracking and streamline analysis were used to generate connectivity matrices from the DWI datasets overlaid to compute distances and highlight disconnectivity patterns in conjunction with other fiber tracking metrics: Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tractography" title="tractography">tractography</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusion%20weighted%20imaging" title=" diffusion weighted imaging"> diffusion weighted imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=schizophrenia" title=" schizophrenia"> schizophrenia</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20psychology" title=" evolutionary psychology"> evolutionary psychology</a> </p> <a href="https://publications.waset.org/abstracts/186084/tractography-analysis-and-the-evolutionary-origin-of-schizophrenia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186084.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">49</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">482</span> The Analysis of Application of Green Bonds in New Energy Vehicles in China: From Evolutionary Game Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhang">Jing Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sustainable development in the new energy vehicles field is the requirement of the net zero aim. Green bonds are accepted as a practical financial tool to boost the transformation of relevant enterprises. The paper analyzes the interactions among governments, enterprises of new energy vehicles, and financial institutions by an evolutionary game theory model and offers advice to stakeholders in China. The decision-making subjects of green behavior are affected by experiences, interests, perception ability, and risk preference, so it is difficult for them to be completely rational. Based on the bounded rationality hypothesis, this paper applies prospect theory in the evolutionary game analysis framework and analyses the costs of government regulation of enterprises adopting green bonds. The influence of the perceived value of revenue prospect and the probability and risk transfer coefficient of the government's active regulation on the decision-making agent's strategy is verified by numerical simulation. Finally, according to the research conclusions, policy suggestions are given to promote green bonds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=green%20bonds" title="green bonds">green bonds</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20energy%20vehicles" title=" new energy vehicles"> new energy vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20development" title=" sustainable development"> sustainable development</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20Game%20Theory%20model" title=" evolutionary Game Theory model"> evolutionary Game Theory model</a> </p> <a href="https://publications.waset.org/abstracts/155309/the-analysis-of-application-of-green-bonds-in-new-energy-vehicles-in-china-from-evolutionary-game-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155309.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">481</span> A Holistic Study of the Beta Lyrae Systems V0487 Lac, V0566 Hya and V0666 Lac</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moqbil%20S.%20Alenazi">Moqbil S. Alenazi</a>, <a href="https://publications.waset.org/abstracts/search?q=Magdy.%20M.%20Elkhateeb"> Magdy. M. Elkhateeb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comprehensive photometric study and evolutionary state for the newly discovered Beta Lyr systems V0487 Lac, V0566 Hya, and V0666 Lac were carried out by means of their first photometric observations. New times of minima were estimated from the observed light curves, and first (O-C) curves were established for all systems. A windows interface version of the Wilson and Devinney code (W-D) based on model atmospheres and a pass band prescription have been used for the radiative treatment. The accepted models reveal some absolute parameters for the studied systems, which are used in adopting the spectral type of the system's components and their evolutionary status. Distances to each system were calculated, and physical properties were estimated. Locations of the systems on the theoreticalmass鈥搇uminosity and mass鈥搑adius relations revealed a good fit for all systems components except for the secondary component of the system V0487 Lac. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eclipsing%20binaries" title="eclipsing binaries">eclipsing binaries</a>, <a href="https://publications.waset.org/abstracts/search?q=light%20curve%20modelling" title=" light curve modelling"> light curve modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20state" title=" evolutionary state"> evolutionary state</a> </p> <a href="https://publications.waset.org/abstracts/163254/a-holistic-study-of-the-beta-lyrae-systems-v0487-lac-v0566-hya-and-v0666-lac" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163254.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">480</span> Molecular Evolutionary Relationships Between O-Antigens of Enteric Bacteria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuriy%20A.%20Knirel">Yuriy A. Knirel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Enteric bacteria Escherichia coli is the predominant facultative anaerobe of the colonic flora, and some specific serotypes are associated with enteritis, hemorrhagic colitis, and hemolytic uremic syndrome. Shigella spp. are human pathogens that cause diarrhea and bacillary dysentery (shigellosis). They are in effect E. coli with a specific mode of pathogenicity. Strains of Salmonella enterica are responsible for a food-borne infection (salmonellosis), and specific serotypes cause typhoid fever and paratyphoid fever. All these bacteria are closely related in respect to structure and genetics of the lipopolysaccharide, including the O-polysaccharide part (O鈥慳ntigen). Being exposed to the bacterial cell surface, the O antigen is subject to intense selection by the host immune system and bacteriophages giving rise to diverse O鈥慳ntigen forms and providing the basis for typing of bacteria. The O-antigen forms of many bacteria are unique, but some are structurally and genetically related to others. The sequenced O-antigen gene clusters between conserved galF and gnd genes were analyzed taking into account the O-antigen structures established by us and others for all S. enterica and Shigella and most E. coli O-serogroups. Multiple genetic mechanisms of diversification of the O-antigen forms, such as lateral gene transfer and mutations, were elucidated and are summarized in the present paper. They include acquisition or inactivation of genes for sugar synthesis or transfer or recombination of O-antigen gene clusters or their parts. The data obtained contribute to our understanding of the origins of the O鈥慳ntigen diversity, shed light on molecular evolutionary relationships between the O-antigens of enteric bacteria, and open a way for studies of the role of gene polymorphism in pathogenicity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enteric%20bacteria" title="enteric bacteria">enteric bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=O-antigen%20gene%20cluster" title=" O-antigen gene cluster"> O-antigen gene cluster</a>, <a href="https://publications.waset.org/abstracts/search?q=polysaccharide%20biosynthesis" title=" polysaccharide biosynthesis"> polysaccharide biosynthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=polysaccharide%20structure" title=" polysaccharide structure"> polysaccharide structure</a> </p> <a href="https://publications.waset.org/abstracts/93781/molecular-evolutionary-relationships-between-o-antigens-of-enteric-bacteria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93781.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">479</span> Comparative Study of Deep Reinforcement Learning Algorithm Against Evolutionary Algorithms for Finding the Optimal Values in a Simulated Environment Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Paranjape">Akshay Paranjape</a>, <a href="https://publications.waset.org/abstracts/search?q=Nils%20Plettenberg"> Nils Plettenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Schmitt"> Robert Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20process%20optimization" title=" production process optimization"> production process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20optimization" title=" real-time optimization"> real-time optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid-MPO" title=" hybrid-MPO"> hybrid-MPO</a> </p> <a href="https://publications.waset.org/abstracts/159906/comparative-study-of-deep-reinforcement-learning-algorithm-against-evolutionary-algorithms-for-finding-the-optimal-values-in-a-simulated-environment-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159906.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">112</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">478</span> Discriminant Analysis as a Function of Predictive Learning to Select Evolutionary Algorithms in Intelligent Transportation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20A.%20Ruiz-Vanoye">Jorge A. Ruiz-Vanoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Ocotl%C3%A1n%20D%C3%ADaz-Parra"> Ocotl谩n D铆az-Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Alejandro%20Fuentes-Penna"> Alejandro Fuentes-Penna</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20V%C3%A9lez-D%C3%ADaz"> Daniel V茅lez-D铆az</a>, <a href="https://publications.waset.org/abstracts/search?q=Edith%20Olaco%20Garc%C3%ADa"> Edith Olaco Garc铆a</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intelligent%20Transportation%20Systems" title="Intelligent Transportation Systems">Intelligent Transportation Systems</a>, <a href="https://publications.waset.org/abstracts/search?q=data-mining%20techniques" title=" data-mining techniques"> data-mining techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/42737/discriminant-analysis-as-a-function-of-predictive-learning-to-select-evolutionary-algorithms-in-intelligent-transportation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42737.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">472</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">477</span> Genetics, Law and Society: Regulating New Genetic Technologies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisling%20De%20Paor">Aisling De Paor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Scientific and technological developments are driving genetics and genetic technologies into the public sphere. Scientists are making genetic discoveries as to the make up of the human body and the cause and effect of disease, diversity and disability amongst individuals. Technological innovation in the field of genetics is also advancing, with the development of genetic testing, and other emerging genetic technologies, including gene editing (which offers the potential for genetic modification). In addition to the benefits for medicine, health care and humanity, these genetic advances raise a range of ethical, legal and societal concerns. From an ethical perspective, such advances may, for example, change the concept of humans and what it means to be human. Science may take over in conceptualising human beings, which may push the boundaries of existing human rights. New genetic technologies, particularly gene editing techniques create the potential to stigmatise disability, by highlighting disability or genetic difference as something that should be eliminated or anticipated. From a disability perspective, use (and misuse) of genetic technologies raise concerns about discrimination and violations to the dignity and integrity of the individual. With an acknowledgement of the likely future orientation of genetic science, and in consideration of the intersection of genetics and disability, this paper highlights the main concerns raised as genetic science and technology advances (particularly with gene editing developments), and the consequences for disability and human rights. Through the use of traditional doctrinal legal methodologies, it investigates the use (and potential misuse) of gene editing as creating the potential for a unique form of discrimination and stigmatization to develop, as well as a potential gateway to a form of new, subtle eugenics. This article highlights the need to maintain caution as to the use, application and the consequences of genetic technologies. With a focus on the law and policy position in Europe, it examines the need to control and regulate these new technologies, particularly gene editing. In addition to considering the need for regulation, this paper highlights non-normative approaches to address this area, including awareness raising and education, public discussion and engagement with key stakeholders in the field and the development of a multifaceted genetics advisory network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disability" title="disability">disability</a>, <a href="https://publications.waset.org/abstracts/search?q=gene-editing" title=" gene-editing"> gene-editing</a>, <a href="https://publications.waset.org/abstracts/search?q=genetics" title=" genetics"> genetics</a>, <a href="https://publications.waset.org/abstracts/search?q=law" title=" law"> law</a>, <a href="https://publications.waset.org/abstracts/search?q=regulation" title=" regulation"> regulation</a> </p> <a href="https://publications.waset.org/abstracts/66469/genetics-law-and-society-regulating-new-genetic-technologies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">476</span> Analyzing Test Data Generation Techniques Using Evolutionary Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arslan%20Ellahi">Arslan Ellahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Amjad%20Hussain"> Syed Amjad Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software Testing is a vital process in software development life cycle. We can attain the quality of software after passing it through software testing phase. We have tried to find out automatic test data generation techniques that are a key research area of software testing to achieve test automation that can eventually decrease testing time. In this paper, we review some of the approaches presented in the literature which use evolutionary search based algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO), etc. to validate the test data generation process. We also look into the quality of test data generation which increases or decreases the efficiency of testing. We have proposed test data generation techniques for model-based testing. We have worked on tuning and fitness function of PSO algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20based" title="search based">search based</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithm" title=" evolutionary algorithm"> evolutionary algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20data%20generation" title=" test data generation"> test data generation</a> </p> <a href="https://publications.waset.org/abstracts/92727/analyzing-test-data-generation-techniques-using-evolutionary-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92727.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">475</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15843.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">564</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">474</span> Dynamic Construction Site Layout Using Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yassir%20AbdelRazig">Yassir AbdelRazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony" title="ant colony">ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20site%20layout" title=" construction site layout"> construction site layout</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a> </p> <a href="https://publications.waset.org/abstracts/28641/dynamic-construction-site-layout-using-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">473</span> Study on Horizontal Ecological Compensation Mechanism in Yangtze River Economic Belt Basin: Based on Evolutionary Game Analysis and Water Quality and Quantity Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tingyu%20Zhang">Tingyu Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The horizontal ecological compensation (HEC) mechanism is the key to stimulating the active participation of the whole basin in ecological protection. In this paper, we construct an evolutionary model for HEC in the Yangtze River Economic Belt (YREB) basin with the introduction of the central government constraint and incentive mechanism (CGCIM) and explore the conditions for the realization of a (Protection and compensation) strategy that meets the social expectations. Further, the water quality-water quantity model is utilized to measure the HEC amount with the characteristic factual data of the YREB in 2020-2022. The results show that the stability of the evolutionary game model of upstream and downstream governments in the YREB is closely related to the CGCIM. If (Protection Compensation) is to be realized as the only evolutionary stable strategy of the evolutionary game system composed of upstream and downstream governments, it is necessary for the CGCIM to satisfy that the sum of the incentives for the protection side and its unilateral or bilateral constraints is greater than twice the input cost of the active strategy, and the sum of the incentives for the compensation side and its unilateral or bilateral constraints is greater than the amount of ecological compensation that needs to be paid by it when it adopts the active strategy. At this point, the total amount of HEC that the downstream government should give to the upstream government of the YREB is 2856.7 million yuan in 2020, 5782.1 million yuan in 2021, and 23166.7 million yuan in 2022. The results of the study can provide a reference for promoting the improvement and refinement of the HEC mechanism in the YREB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=horizontal%20ecological%20compensation" title="horizontal ecological compensation">horizontal ecological compensation</a>, <a href="https://publications.waset.org/abstracts/search?q=Yangtze%20river%20economic%20belt" title=" Yangtze river economic belt"> Yangtze river economic belt</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20game%20analysis" title=" evolutionary game analysis"> evolutionary game analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20and%20quantity%20model%20research%20on%20territorial%20ecological%20restoration%20in%20Mianzhu%20city" title=" water quality and quantity model research on territorial ecological restoration in Mianzhu city"> water quality and quantity model research on territorial ecological restoration in Mianzhu city</a>, <a href="https://publications.waset.org/abstracts/search?q=Sichuan" title=" Sichuan"> Sichuan</a>, <a href="https://publications.waset.org/abstracts/search?q=under%20the%20dual%20evaluation%20framework" title=" under the dual evaluation framework"> under the dual evaluation framework</a> </p> <a href="https://publications.waset.org/abstracts/185516/study-on-horizontal-ecological-compensation-mechanism-in-yangtze-river-economic-belt-basin-based-on-evolutionary-game-analysis-and-water-quality-and-quantity-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185516.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">49</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">472</span> Solving Process Planning, Weighted Earliest Due Date Scheduling and Weighted Due Date Assignment Using Simulated Annealing and Evolutionary Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Hulusi%20Kokcam"> Abdullah Hulusi Kokcam</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuat%20Simsir"> Fuat Simsir</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zer%20Uygun"> 脰zer Uygun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally, three important manufacturing functions which are process planning, scheduling and due-date assignment are performed sequentially and separately. Although there are numerous works on the integration of process planning and scheduling and plenty of works focusing on scheduling with due date assignment, there are only a few works on integrated process planning, scheduling and due-date assignment. Although due-dates are determined without taking into account of weights of the customers in the literature, here weighted due-date assignment is employed to get better performance. Jobs are scheduled according to weighted earliest due date dispatching rule and due dates are determined according to some popular due date assignment methods by taking into account of the weights of each job. Simulated Annealing, Evolutionary Strategies, Random Search, hybrid of Random Search and Simulated Annealing, and hybrid of Random Search and Evolutionary Strategies, are applied as solution techniques. Three important manufacturing functions are integrated step-by-step and higher integration levels are found better. Search meta-heuristics are found to be very useful while improving performance measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20strategies" title=" evolutionary strategies"> evolutionary strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20searches" title=" hybrid searches"> hybrid searches</a> </p> <a href="https://publications.waset.org/abstracts/67706/solving-process-planning-weighted-earliest-due-date-scheduling-and-weighted-due-date-assignment-using-simulated-annealing-and-evolutionary-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67706.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">471</span> Research on Models and Selection of Entry Strategies for Catering Industry Based on the Evolutionary Game Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianxin%20Zhu">Jianxin Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Na%20Liu"> Na Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Entry strategies play a vital role in the development of new enterprises in the catering industry. Different entry strategies will have different effects on the development of new enterprise. Based on the research of scholars at home and abroad, and combining the characteristics of the catering industry, the entry strategies are divided into low-price entry strategies and high-quality entry strategies. Facing the entry of new enterprise, the strategies of incumbent enterprises are divided into response strategies and non-response strategies. This paper uses evolutionary game theory to study the strategic interaction mechanism between incumbent companies and new enterprises. When different initial values and parameter values are set, which strategy will the two-game subjects choose, respectively? Using matlab2016 for numerical simulation, the results show that the choice of strategies for new enterprise and incumbent enterprise is influenced by more than one factor, and the system has different evolution trends under different circumstances. When the parameters were set, the choice of two subjects' strategies mainly depends on the net profit between the strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=catering%20industry" title="catering industry">catering industry</a>, <a href="https://publications.waset.org/abstracts/search?q=entry%20strategy" title=" entry strategy"> entry strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20game" title=" evolutionary game"> evolutionary game</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20interaction%20mechanism" title=" strategic interaction mechanism"> strategic interaction mechanism</a> </p> <a href="https://publications.waset.org/abstracts/121230/research-on-models-and-selection-of-entry-strategies-for-catering-industry-based-on-the-evolutionary-game-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">470</span> Downscaling Daily Temperature with Neuroevolutionary Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Shi">Min Shi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> State of the art research with Artificial Neural Networks for the downscaling of General Circulation Models (GCMs) mainly uses back-propagation algorithm as a training approach. This paper introduces another training approach of ANNs, Evolutionary Algorithm. The combined algorithm names neuroevolutionary (NE) algorithm. We investigate and evaluate the use of the NE algorithms in statistical downscaling by generating temperature estimates at interior points given information from a lattice of surrounding locations. The results of our experiments indicate that NE algorithms can be efficient alternative downscaling methods for daily temperatures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temperature" title="temperature">temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=downscaling" title=" downscaling"> downscaling</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/29051/downscaling-daily-temperature-with-neuroevolutionary-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29051.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">469</span> Sensor Network Routing Optimization by Simulating Eurygaster Life in Wheat Farms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Ahmadi">Fariborz Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Salehi"> Hamid Salehi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khosrow%20Karimi"> Khosrow Karimi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title="evolutionary computation">evolutionary computation</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20network%20optimization" title=" sensor network optimization"> sensor network optimization</a> </p> <a href="https://publications.waset.org/abstracts/41373/sensor-network-routing-optimization-by-simulating-eurygaster-life-in-wheat-farms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41373.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetics&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetics&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetics&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetics&page=5">5</a></li> <li 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