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algorithms</title> <meta name="description" content="Search results for: evolutionary algorithms"> <meta name="keywords" content="evolutionary algorithms"> <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 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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 algorithms"> <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> 2307</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: evolutionary algorithms</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2307</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">469</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">2306</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">2305</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">2304</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">483</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">2303</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">2302</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">445</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">2301</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">2300</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">382</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">2299</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">2298</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. Peñaloza 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">2297</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"> Ocotlán Díaz-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 Vélez-Díaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Edith%20Olaco%20Garc%C3%ADa"> Edith Olaco García</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">2296</span> Terraria AI: YOLO Interface for Decision-Making Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Barrantes%20Chaves">Emmanuel Barrantes Chaves</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernesto%20Rivera%20Alvarado"> Ernesto Rivera Alvarado</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method to enable agents for the Terraria game to evaluate algorithms commonly used in general video game artificial intelligence competitions. The usage of the ‘You Only Look Once’ model in the first layer of the process obtains information from the screen, translating this information into a video game description language known as “Video Game Description Language”; the agents take that as input to make decisions. For this, the state-of-the-art algorithms were tested and compared; Monte Carlo Tree Search and Rolling Horizon Evolutionary; in this case, Rolling Horizon Evolutionary shows a better performance. This approach’s main advantage is that a VGDL beforehand is unnecessary. It will be built on the fly and opens the road for using more games as a framework for AI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI" title="AI">AI</a>, <a href="https://publications.waset.org/abstracts/search?q=MCTS" title=" MCTS"> MCTS</a>, <a href="https://publications.waset.org/abstracts/search?q=RHEA" title=" RHEA"> RHEA</a>, <a href="https://publications.waset.org/abstracts/search?q=Terraria" title=" Terraria"> Terraria</a>, <a href="https://publications.waset.org/abstracts/search?q=VGDL" title=" VGDL"> VGDL</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv5" title=" YOLOv5"> YOLOv5</a> </p> <a href="https://publications.waset.org/abstracts/168302/terraria-ai-yolo-interface-for-decision-making-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168302.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">95</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">2295</span> A High-Level Co-Evolutionary Hybrid Algorithm for the Multi-Objective Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid distributed algorithm has been suggested for the multi-objective job shop scheduling problem. Many new approaches are used at design steps of the distributed algorithm. Co-evolutionary structure of the algorithm and competition between different communicated hybrid algorithms, which are executed simultaneously, causes to efficient search. Using several machines for distributing the algorithms, at the iteration and solution levels, increases computational speed. The proposed algorithm is able to find the Pareto solutions of the big problems in shorter time than other algorithm in the literature. Apache Spark and Hadoop platforms have been used for the distribution of the algorithm. The suggested algorithm and implementations have been compared with results of the successful algorithms in the literature. Results prove the efficiency and high speed of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark" title=" Apache Spark"> Apache Spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72317/a-high-level-co-evolutionary-hybrid-algorithm-for-the-multi-objective-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72317.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">363</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">2294</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">189</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">2293</span> An Expert System Designed to Be Used with MOEAs for Efficient Portfolio Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Liagkouras"> Kostas Liagkouras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expert%20systems" title="expert systems">expert systems</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=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title=" portfolio selection"> portfolio selection</a> </p> <a href="https://publications.waset.org/abstracts/3509/an-expert-system-designed-to-be-used-with-moeas-for-efficient-portfolio-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3509.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">439</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">2292</span> A Study on Computational Fluid Dynamics (CFD)-Based Design Optimization Techniques Using Multi-Objective Evolutionary Algorithms (MOEA)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20E.%20Hodaib">Ahmed E. Hodaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Hashem"> Mohamed A. Hashem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In engineering applications, a design has to be as fully perfect as possible in some defined case. The designer has to overcome many challenges in order to reach the optimal solution to a specific problem. This process is called optimization. Generally, there is always a function called “objective function” that is required to be maximized or minimized by choosing input parameters called “degrees of freedom” within an allowed domain called “search space” and computing the values of the objective function for these input values. It becomes more complex when we have more than one objective for our design. As an example for Multi-Objective Optimization Problem (MOP): A structural design that aims to minimize weight and maximize strength. In such case, the Pareto Optimal Frontier (POF) is used, which is a curve plotting two objective functions for the best cases. At this point, a designer should make a decision to choose the point on the curve. Engineers use algorithms or iterative methods for optimization. In this paper, we will discuss the Evolutionary Algorithms (EA) which are widely used with Multi-objective Optimization Problems due to their robustness, simplicity, suitability to be coupled and to be parallelized. Evolutionary algorithms are developed to guarantee the convergence to an optimal solution. An EA uses mechanisms inspired by Darwinian evolution principles. Technically, they belong to the family of trial and error problem solvers and can be considered global optimization methods with a stochastic optimization character. The optimization is initialized by picking random solutions from the search space and then the solution progresses towards the optimal point by using operators such as Selection, Combination, Cross-over and/or Mutation. These operators are applied to the old solutions “parents” so that new sets of design variables called “children” appear. The process is repeated until the optimal solution to the problem is reached. Reliable and robust computational fluid dynamics solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbo-machinery, and auto-motives. Coupling of Computational Fluid Dynamics “CFD” and Multi-Objective Evolutionary Algorithms “MOEA” has become substantial in aerospace engineering applications, such as in aerodynamic shape optimization and advanced turbo-machinery design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20optimization" title="mathematical optimization">mathematical optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20evolutionary%20algorithms%20%22MOEA%22" title=" multi-objective evolutionary algorithms "MOEA""> multi-objective evolutionary algorithms "MOEA"</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20fluid%20dynamics%20%22CFD%22" title=" computational fluid dynamics "CFD""> computational fluid dynamics "CFD"</a>, <a href="https://publications.waset.org/abstracts/search?q=aerodynamic%20shape%20optimization" title=" aerodynamic shape optimization"> aerodynamic shape optimization</a> </p> <a href="https://publications.waset.org/abstracts/54515/a-study-on-computational-fluid-dynamics-cfd-based-design-optimization-techniques-using-multi-objective-evolutionary-algorithms-moea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54515.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">256</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">2291</span> Two Points Crossover Genetic Algorithm for Loop Layout Design Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xu%20LiYun">Xu LiYun</a>, <a href="https://publications.waset.org/abstracts/search?q=Briand%20Florent"> Briand Florent</a>, <a href="https://publications.waset.org/abstracts/search?q=Fan%20GuoLiang"> Fan GuoLiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crossover" title="crossover">crossover</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=layout%20design%20problem" title=" layout design problem"> layout design problem</a>, <a href="https://publications.waset.org/abstracts/search?q=loop-layout" title=" loop-layout"> loop-layout</a>, <a href="https://publications.waset.org/abstracts/search?q=manufacturing%20optimization" title=" manufacturing optimization"> manufacturing optimization</a> </p> <a href="https://publications.waset.org/abstracts/75351/two-points-crossover-genetic-algorithm-for-loop-layout-design-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75351.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">279</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">2290</span> Image Encryption Using Eureqa to Generate an Automated Mathematical Key</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halima%20Adel%20Halim%20Shnishah">Halima Adel Halim Shnishah</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Mulvaney"> David Mulvaney</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Applying traditional symmetric cryptography algorithms while computing encryption and decryption provides immunity to secret keys against different attacks. One of the popular techniques generating automated secret keys is evolutionary computing by using Eureqa API tool, which got attention in 2013. In this paper, we are generating automated secret keys for image encryption and decryption using Eureqa API (tool which is used in evolutionary computing technique). Eureqa API models pseudo-random input data obtained from a suitable source to generate secret keys. The validation of generated secret keys is investigated by performing various statistical tests (histogram, chi-square, correlation of two adjacent pixels, correlation between original and encrypted images, entropy and key sensitivity). Experimental results obtained from methods including histogram analysis, correlation coefficient, entropy and key sensitivity, show that the proposed image encryption algorithms are secure and reliable, with the potential to be adapted for secure image communication applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20encryption%20algorithms" title="image encryption algorithms">image encryption algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Eureqa" title=" Eureqa"> Eureqa</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20measurements" title=" statistical measurements"> statistical measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20key%20generation" title=" automated key generation"> automated key generation</a> </p> <a href="https://publications.waset.org/abstracts/79042/image-encryption-using-eureqa-to-generate-an-automated-mathematical-key" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79042.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">482</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">2289</span> Reinforcement Learning Optimization: Unraveling Trends and Advancements in Metaheuristic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Paul">Rahul Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=Kedar%20Nath%20Das"> Kedar Nath Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <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=loss%20function" title=" loss function"> loss function</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20optimization%20techniques" title=" evolutionary optimization techniques"> evolutionary optimization techniques</a> </p> <a href="https://publications.waset.org/abstracts/170676/reinforcement-learning-optimization-unraveling-trends-and-advancements-in-metaheuristic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170676.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">74</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">2288</span> Scheduling in Cloud Networks Using Chakoos Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masoumeh%20Ali%20Pouri">Masoumeh Ali Pouri</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Haj%20Seyyed%20Javadi"> Hamid Haj Seyyed Javadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, cloud processing is one of the important issues in information technology. Since scheduling of tasks graph is an NP-hard problem, considering approaches based on undeterminisitic methods such as evolutionary processing, mostly genetic and cuckoo algorithms, will be effective. Therefore, an efficient algorithm has been proposed for scheduling of tasks graph to obtain an appropriate scheduling with minimum time. In this algorithm, the new approach is based on making the length of the critical path shorter and reducing the cost of communication. Finally, the results obtained from the implementation of the presented method show that this algorithm acts the same as other algorithms when it faces graphs without communication cost. It performs quicker and better than some algorithms like DSC and MCP algorithms when it faces the graphs involving communication cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=tasks%20graph" title=" tasks graph"> tasks graph</a>, <a href="https://publications.waset.org/abstracts/search?q=chakoos%20algorithm" title=" chakoos algorithm"> chakoos algorithm</a> </p> <a href="https://publications.waset.org/abstracts/175869/scheduling-in-cloud-networks-using-chakoos-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175869.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">64</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">2287</span> SMART: Solution Methods with Ants Running by Types</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Zufferey">Nicolas Zufferey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant algorithms are well-known metaheuristics which have been widely used since two decades. In most of the literature, an ant is a constructive heuristic able to build a solution from scratch. However, other types of ant algorithms have recently emerged: the discussion is thus not limited by the common framework of the constructive ant algorithms. Generally, at each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (also called the visibility, the short term profit or the heuristic information) and the trail system (central memory which collects historical information of the search process). Usually, all the ants of the population have the same characteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed, namely SMART (for Solution Methods with Ants Running by Types). It relies on the use of different population of ants, where each population has its own personality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20algorithms" title="ant algorithms">ant algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20procedures" title=" evolutionary procedures"> evolutionary procedures</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=population-based%20methods" title=" population-based methods"> population-based methods</a> </p> <a href="https://publications.waset.org/abstracts/36375/smart-solution-methods-with-ants-running-by-types" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36375.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">365</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">2286</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. Jiménez</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20J%C3%B3dar"> R. Jódar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mart%C3%ADn"> M. Martín</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S%C3%A1nchez"> G. Sánchez</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">370</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">2285</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">563</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">2284</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">2283</span> An Adaptive Hybrid Surrogate-Assisted Particle Swarm Optimization Algorithm for Expensive Structural Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiongxiong%20You">Xiongxiong You</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhanwen%20Niu"> Zhanwen Niu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20selection" title="adaptive selection">adaptive selection</a>, <a href="https://publications.waset.org/abstracts/search?q=expensive%20optimization" title=" expensive optimization"> expensive optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=rotor%20system" title=" rotor system"> rotor system</a>, <a href="https://publications.waset.org/abstracts/search?q=surrogates%20assisted%20evolutionary%20algorithms" title=" surrogates assisted evolutionary algorithms"> surrogates assisted evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/137516/an-adaptive-hybrid-surrogate-assisted-particle-swarm-optimization-algorithm-for-expensive-structural-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137516.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">141</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">2282</span> Review of Hydrologic Applications of Conceptual Models for Precipitation-Runoff Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oluwatosin%20Olofintoye">Oluwatosin Olofintoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Josiah%20Adeyemo"> Josiah Adeyemo</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbemileke%20Shomade"> Gbemileke Shomade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</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>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20training%20method" title=" gradient training method"> gradient training method</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall-runoff%20model" title=" rainfall-runoff model"> rainfall-runoff model</a> </p> <a href="https://publications.waset.org/abstracts/40481/review-of-hydrologic-applications-of-conceptual-models-for-precipitation-runoff-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40481.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">454</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">2281</span> Evolution under Length Constraints for Convolutional Neural Networks Architecture Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ousmane%20Youme">Ousmane Youme</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Marie%20Dembele"> Jean Marie Dembele</a>, <a href="https://publications.waset.org/abstracts/search?q=Eugene%20Ezin"> Eugene Ezin</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Cambier"> Christophe Cambier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN%20architecture" title="CNN architecture">CNN architecture</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=evolution%20algorithm" title=" evolution algorithm"> evolution algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=length%20constraints" title=" length constraints"> length constraints</a> </p> <a href="https://publications.waset.org/abstracts/154373/evolution-under-length-constraints-for-convolutional-neural-networks-architecture-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154373.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">128</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">2280</span> Geospatial Network Analysis Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varun%20Singh">Varun Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mainak%20Bandyopadhyay"> Mainak Bandyopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Maharana%20Pratap%20Singh"> Maharana Pratap Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS. <p class="card-text"><strong>Keywords:</strong> <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=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20data" title=" traffic data"> traffic data</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a> </p> <a href="https://publications.waset.org/abstracts/13181/geospatial-network-analysis-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13181.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">483</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">2279</span> Transfer Knowledge From Multiple Source Problems to a Target Problem in Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Terence%20Soule">Terence Soule</a>, <a href="https://publications.waset.org/abstracts/search?q=Tami%20Al%20Ghamdi"> Tami Al Ghamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</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=evolutionary%20computation" title=" evolutionary computation"> evolutionary computation</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20and%20target" title=" source and target"> source and target</a> </p> <a href="https://publications.waset.org/abstracts/147927/transfer-knowledge-from-multiple-source-problems-to-a-target-problem-in-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147927.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">140</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">2278</span> A Hybrid Distributed Algorithm for Multi-Objective Dynamic Flexible Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid distributed algorithm has been suggested for multi-objective dynamic flexible job shop scheduling problem. The proposed algorithm is high level, in which several algorithms search the space on different machines simultaneously also it is a hybrid algorithm that takes advantages of the artificial intelligence, evolutionary and optimization methods. Distribution is done at different levels and new approaches are used for design of the algorithm. Apache spark and Hadoop frameworks have been used for the distribution of the algorithm. The Pareto optimality approach is used for solving the multi-objective benchmarks. The suggested algorithm that is able to solve large-size problems in short times has been compared with the successful algorithms of the literature. The results prove high speed and efficiency of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=apache-spark" title=" apache-spark"> apache-spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20dynamic%20job%20shop%20scheduling" title=" flexible dynamic job shop scheduling"> flexible dynamic job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72319/a-hybrid-distributed-algorithm-for-multi-objective-dynamic-flexible-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72319.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">354</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%20algorithms&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms&page=4">4</a></li> <li class="page-item"><a class="page-link" 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