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genetic algorithms</title> <meta name="description" content="Search results for: hybrid genetic algorithms"> <meta name="keywords" content="hybrid genetic 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 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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="hybrid genetic 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> 4984</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: hybrid genetic algorithms</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4984</span> Prediction of the Tunnel Fire Flame Length by Hybrid Model of Neural Network and Genetic Algorithms </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behzad%20Niknam">Behzad Niknam</a>, <a href="https://publications.waset.org/abstracts/search?q=Kourosh%20Shahriar"> Kourosh Shahriar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Madani"> Hassan Madani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper demonstrates the applicability of Hybrid Neural Networks that combine with back propagation networks (BPN) and Genetic Algorithms (GAs) for predicting the flame length of tunnel fire A hybrid neural network model has been developed to predict the flame length of tunnel fire based parameters such as Fire Heat Release rate, air velocity, tunnel width, height and cross section area. The network has been trained with experimental data obtained from experimental work. The hybrid neural network model learned the relationship for predicting the flame length in just 3000 training epochs. After successful learning, the model predicted the flame length. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tunnel%20fire" title="tunnel fire">tunnel fire</a>, <a href="https://publications.waset.org/abstracts/search?q=flame%20length" title=" flame length"> flame length</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</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/10980/prediction-of-the-tunnel-fire-flame-length-by-hybrid-model-of-neural-network-and-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10980.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">643</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">4983</span> Hybrid Genetic Approach for Solving Economic Dispatch Problems with Valve-Point Effect</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20I.%20Mahrous">Mohamed I. Mahrous</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20G.%20Ashmawy"> Mohamed G. Ashmawy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hybrid genetic algorithm (HGA) is proposed in this paper to determine the economic scheduling of electric power generation over a fixed time period under various system and operational constraints. The proposed technique can outperform conventional genetic algorithms (CGAs) in the sense that HGA make it possible to improve both the quality of the solution and reduce the computing expenses. In contrast, any carefully designed GA is only able to balance the exploration and the exploitation of the search effort, which means that an increase in the accuracy of a solution can only occure at the sacrifice of convergent speed, and vice visa. It is unlikely that both of them can be improved simultaneously. The proposed hybrid scheme is developed in such a way that a simple GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and a local search method (pattern search technique) is next employed to do the fine tuning. The aim of the strategy is to achieve the cost reduction within a reasonable computing time. The effectiveness of the proposed hybrid technique is verified on two real public electricity supply systems with 13 and 40 generator units respectively. The simulation results obtained with the HGA for the two real systems are very encouraging with regard to the computational expenses and the cost reduction of power generation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title="genetic algorithms">genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20dispatch" title=" economic dispatch"> economic dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20search" title=" pattern search"> pattern search</a> </p> <a href="https://publications.waset.org/abstracts/14138/hybrid-genetic-approach-for-solving-economic-dispatch-problems-with-valve-point-effect" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14138.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">444</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">4982</span> Task Scheduling on Parallel System Using Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jasbir%20Singh%20Gill">Jasbir Singh Gill</a>, <a href="https://publications.waset.org/abstracts/search?q=Baljit%20Singh"> Baljit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Scheduling and mapping the application task graph on multiprocessor parallel systems is considered as the most crucial and critical NP-complete problem. Many genetic algorithms have been proposed to solve such problems. In this paper, two genetic approach based algorithms have been designed and developed with or without task duplication. The proposed algorithms work on two fitness functions. The first fitness i.e. task fitness is used to minimize the total finish time of the schedule (schedule length) while the second fitness function i.e. process fitness is concerned with allocating the tasks to the available highly efficient processor from the list of available processors (load balance). Proposed genetic-based algorithms have been experimentally implemented and evaluated with other state-of-art popular and widely used algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20computing" title="parallel computing">parallel computing</a>, <a href="https://publications.waset.org/abstracts/search?q=task%20scheduling" title=" task scheduling"> task scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=task%20duplication" title=" task duplication"> task duplication</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/75569/task-scheduling-on-parallel-system-using-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75569.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">348</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">4981</span> Application of Genetic Algorithm with Multiobjective Function to Improve the Efficiency of Photovoltaic Thermal System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonveer%20Singh">Sonveer Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20Agrawal"> Sanjay Agrawal</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20V.%20Avasthi"> D. V. Avasthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayant%20Shekhar"> Jayant Shekhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to improve the efficiency of photovoltaic thermal (PVT) system with the help of Genetic Algorithms with multi-objective function. There are some parameters that affect the efficiency of PVT system like depth and length of the channel, velocity of flowing fluid through the channel, thickness of the tedlar and glass, temperature of inlet fluid i.e. all above parameters are considered for optimization. An attempt has been made to the model and optimizes the parameters of glazed hybrid single channel PVT module when two objective functions have been considered separately. The two objective function for optimization of PVT module is overall electrical and thermal efficiency. All equations for PVT module have been derived. Using genetic algorithms (GAs), above two objective functions of the system has been optimized separately and analysis has been carried out for two cases. Two cases are: Case-I; Improvement in electrical and thermal efficiency when overall electrical efficiency is optimized, Case-II; Improvement in electrical and thermal efficiency when overall thermal efficiency is optimized. All the parameters that are used in genetic algorithms are the parameters that could be changed, and the non-changeable parameters, like solar radiation, ambient temperature cannot be used in the algorithm. It has been observed that electrical efficiency (14.08%) and thermal efficiency (19.48%) are obtained when overall thermal efficiency was an objective function for optimization. It is observed that GA is a very efficient technique to estimate the design parameters of hybrid single channel PVT module. <p class="card-text"><strong>Keywords:</strong> <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=energy" title=" energy"> energy</a>, <a href="https://publications.waset.org/abstracts/search?q=exergy" title=" exergy"> exergy</a>, <a href="https://publications.waset.org/abstracts/search?q=PVT%20module" title=" PVT module"> PVT module</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/16503/application-of-genetic-algorithm-with-multiobjective-function-to-improve-the-efficiency-of-photovoltaic-thermal-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16503.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">605</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">4980</span> Hybrid Adaptive Modeling to Enhance Robustness of Real-Time Optimization </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussain%20Syed%20Asad">Hussain Syed Asad</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Kwok%20Kit%20Yuen"> Richard Kwok Kit Yuen</a>, <a href="https://publications.waset.org/abstracts/search?q=Gongsheng%20Huang"> Gongsheng Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20performance" title="energy performance">energy performance</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20adaptive%20modeling" title=" hybrid adaptive modeling"> hybrid adaptive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20genetic%20algorithms" title=" hybrid genetic algorithms"> hybrid genetic algorithms</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=heating" title=" heating"> heating</a>, <a href="https://publications.waset.org/abstracts/search?q=ventilation" title=" ventilation"> ventilation</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20air-conditioning" title=" and air-conditioning"> and air-conditioning</a> </p> <a href="https://publications.waset.org/abstracts/59904/hybrid-adaptive-modeling-to-enhance-robustness-of-real-time-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59904.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">417</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">4979</span> Hybrid Intelligent Optimization Methods for Optimal Design of Horizontal-Axis Wind Turbine Blades</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Tandis">E. Tandis</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Assareh"> E. Assareh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Blade%20Design" title="Blade Design">Blade Design</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%20Algorithm" title=" Genetic Algorithm"> Genetic Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Bees%20Algorithm" title=" Bees Algorithm"> Bees Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Genetic-Based%20Bees%20Algorithm" title=" Genetic-Based Bees Algorithm"> Genetic-Based Bees Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Large%20Wind%20Turbine" title=" Large Wind Turbine"> Large Wind Turbine</a> </p> <a href="https://publications.waset.org/abstracts/52022/hybrid-intelligent-optimization-methods-for-optimal-design-of-horizontal-axis-wind-turbine-blades" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52022.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">316</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">4978</span> Comparison of ANFIS Update Methods Using Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20R.%20Phangtriastu">Michael R. Phangtriastu</a>, <a href="https://publications.waset.org/abstracts/search?q=Herriyandi%20Herriyandi"> Herriyandi Herriyandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Diaz%20D.%20Santika"> Diaz D. Santika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</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=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/68821/comparison-of-anfis-update-methods-using-genetic-algorithm-particle-swarm-optimization-and-artificial-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68821.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">352</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">4977</span> Optimal Design of Redundant Hybrid Manipulator for Minimum Singularity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arash%20Rahmani">Arash Rahmani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Ghanbari"> Ahmad Ghanbari</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Baghernezhad"> Abbas Baghernezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Babak%20Safaei"> Babak Safaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the design of parallel manipulators, usually mean value of a dexterity measure over the workspace volume is considered as the objective function to be used in optimization algorithms. The mentioned indexes in a hybrid parallel manipulator (HPM) are quite complicated to solve thanks to infinite solutions for every point within the workspace of the redundant manipulators. In this paper, spatial isotropic design axioms are extended as a well-known method for optimum design of manipulators. An upper limit for the isotropy measure of HPM is calculated and instead of computing and minimizing isotropy measure, minimizing the obtained limit is considered. To this end, two different objective functions are suggested which are obtained from objective functions of comprising modules. Finally, by using genetic algorithm (GA), the best geometric parameters for a specific hybrid parallel robot which is composed of two modified Gough-Stewart platforms (MGSP) are achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20manipulator" title="hybrid manipulator">hybrid manipulator</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20isotropy" title=" spatial isotropy"> spatial isotropy</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=optimum%20design" title=" optimum design"> optimum design</a> </p> <a href="https://publications.waset.org/abstracts/41885/optimal-design-of-redundant-hybrid-manipulator-for-minimum-singularity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41885.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">336</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">4976</span> An Experimental Study on Some Conventional and Hybrid Models of Fuzzy Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeugert%20Kujtila">Jeugert Kujtila</a>, <a href="https://publications.waset.org/abstracts/search?q=Kristi%20Hoxhalli"> Kristi Hoxhalli</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20Dalipi"> Ramazan Dalipi</a>, <a href="https://publications.waset.org/abstracts/search?q=Erjon%20Cota"> Erjon Cota</a>, <a href="https://publications.waset.org/abstracts/search?q=Ardit%20Murati"> Ardit Murati</a>, <a href="https://publications.waset.org/abstracts/search?q=Erind%20Bedalli"> Erind Bedalli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a versatile instrument in the analysis of collections of data providing insights of the underlying structures of the dataset and enhancing the modeling capabilities. The fuzzy approach to the clustering problem increases the flexibility involving the concept of partial memberships (some value in the continuous interval [0, 1]) of the instances in the clusters. Several fuzzy clustering algorithms have been devised like FCM, Gustafson-Kessel, Gath-Geva, kernel-based FCM, PCM etc. Each of these algorithms has its own advantages and drawbacks, so none of these algorithms would be able to perform superiorly in all datasets. In this paper we will experimentally compare FCM, GK, GG algorithm and a hybrid two-stage fuzzy clustering model combining the FCM and Gath-Geva algorithms. Firstly we will theoretically dis-cuss the advantages and drawbacks for each of these algorithms and we will describe the hybrid clustering model exploiting the advantages and diminishing the drawbacks of each algorithm. Secondly we will experimentally compare the accuracy of the hybrid model by applying it on several benchmark and synthetic datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title="fuzzy clustering">fuzzy clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means%20algorithm%20%28FCM%29" title=" fuzzy c-means algorithm (FCM)"> fuzzy c-means algorithm (FCM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Gustafson-Kessel%20algorithm" title=" Gustafson-Kessel algorithm"> Gustafson-Kessel algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20clustering%20model" title=" hybrid clustering model"> hybrid clustering model</a> </p> <a href="https://publications.waset.org/abstracts/67863/an-experimental-study-on-some-conventional-and-hybrid-models-of-fuzzy-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67863.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">514</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">4975</span> Mutual Information Based Image Registration of Satellite Images Using PSO-GA Hybrid Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dipti%20Patra">Dipti Patra</a>, <a href="https://publications.waset.org/abstracts/search?q=Guguloth%20Uma"> Guguloth Uma</a>, <a href="https://publications.waset.org/abstracts/search?q=Smita%20Pradhan"> Smita Pradhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Registration is a fundamental task in image processing. It is used to transform different sets of data into one coordinate system, where data are acquired from different times, different viewing angles, and/or different sensors. The registration geometrically aligns two images (the reference and target images). Registration techniques are used in satellite images and it is important in order to be able to compare or integrate the data obtained from these different measurements. In this work, mutual information is considered as a similarity metric for registration of satellite images. The transformation is assumed to be a rigid transformation. An attempt has been made here to optimize the transformation function. The proposed image registration technique hybrid PSO-GA incorporates the notion of Particle Swarm Optimization and Genetic Algorithm and is used for finding the best optimum values of transformation parameters. The performance comparision obtained with the experiments on satellite images found that the proposed hybrid PSO-GA algorithm outperforms the other algorithms in terms of mutual information and registration accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20registration" title="image registration">image registration</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=hybrid%20PSO-GA%20algorithm%20and%20mutual%20information" title=" hybrid PSO-GA algorithm and mutual information"> hybrid PSO-GA algorithm and mutual information</a> </p> <a href="https://publications.waset.org/abstracts/9683/mutual-information-based-image-registration-of-satellite-images-using-pso-ga-hybrid-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9683.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">407</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">4974</span> Using Genetic Algorithms and Rough Set Based Fuzzy K-Modes to Improve Centroid Model Clustering Performance on Categorical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rishabh%20Srivastav">Rishabh Srivastav</a>, <a href="https://publications.waset.org/abstracts/search?q=Divyam%20%20Sharma"> Divyam Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=categorical%20data" title="categorical data">categorical data</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</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=K%20modes%20clustering" title=" K modes clustering"> K modes clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title=" rough sets"> rough sets</a> </p> <a href="https://publications.waset.org/abstracts/128558/using-genetic-algorithms-and-rough-set-based-fuzzy-k-modes-to-improve-centroid-model-clustering-performance-on-categorical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128558.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">246</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">4973</span> A Survey of Grammar-Based Genetic Programming and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matthew%20T.%20Wilson">Matthew T. Wilson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper covers a selection of research utilizing grammar-based genetic programming, and illustrates how context-free grammar can be used to constrain genetic programming. It focuses heavily on grammatical evolution, one of the most popular variants of grammar-based genetic programming, and the way its operators and terminals are specialized and modified from those in genetic programming. A variety of implementations of grammatical evolution for general use are covered, as well as research each focused on using grammatical evolution or grammar-based genetic programming on a single application, or to solve a specific problem, including some of the classically considered genetic programming problems, such as the Santa Fe Trail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context-free%20grammar" title="context-free grammar">context-free grammar</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20programming" title=" genetic programming"> genetic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=grammatical%20evolution" title=" grammatical evolution"> grammatical evolution</a> </p> <a href="https://publications.waset.org/abstracts/120249/a-survey-of-grammar-based-genetic-programming-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120249.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">187</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">4972</span> A Dynamic Software Product Line Approach to Self-Adaptive Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelghani%20Alidra">Abdelghani Alidra</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Tahar%20Kimour"> Mohamed Tahar Kimour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithm must adapt themselves at design time to cope with the search problem specific requirements and at runtime to balance exploration and convergence objectives. In a previous article, we have shown that modeling and implementing Genetic Algorithms (GA) using the software product line (SPL) paradigm is very appreciable because they constitute a product family sharing a common base of code. In the present article we propose to extend the use of the feature model of the genetic algorithms family to model the potential states of the GA in what is called a Dynamic Software Product Line. The objective of this paper is the systematic generation of a reconfigurable architecture that supports the dynamic of the GA and which is easily deduced from the feature model. The resultant GA is able to perform dynamic reconfiguration autonomously to fasten the convergence process while producing better solutions. Another important advantage of our approach is the exploitation of recent advances in the domain of dynamic SPLs to enhance the performance of the GAs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-adaptive%20genetic%20algorithms" title="self-adaptive genetic algorithms">self-adaptive genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20engineering" title=" software engineering"> software engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20software%20product%20lines" title=" dynamic software product lines"> dynamic software product lines</a>, <a href="https://publications.waset.org/abstracts/search?q=reconfigurable%20architecture" title=" reconfigurable architecture"> reconfigurable architecture</a> </p> <a href="https://publications.waset.org/abstracts/37396/a-dynamic-software-product-line-approach-to-self-adaptive-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37396.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">285</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">4971</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">4970</span> Sensitivity Analysis during the Optimization Process Using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Rubio">M. A. Rubio</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Urquia"> A. Urquia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithms (GA) are applied to the solution of high-dimensional optimization problems. Additionally, sensitivity analysis (SA) is usually carried out to determine the effect on optimal solutions of changes in parameter values of the objective function. These two analyses (i.e., optimization and sensitivity analysis) are computationally intensive when applied to high-dimensional functions. The approach presented in this paper consists in performing the SA during the GA execution, by statistically analyzing the data obtained of running the GA. The advantage is that in this case SA does not involve making additional evaluations of the objective function and, consequently, this proposed approach requires less computational effort than conducting optimization and SA in two consecutive steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity" title=" sensitivity"> sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20calibration" title=" model calibration"> model calibration</a> </p> <a href="https://publications.waset.org/abstracts/62152/sensitivity-analysis-during-the-optimization-process-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62152.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">436</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">4969</span> A Hybrid Data Mining Algorithm Based System for Intelligent Defence Mission Readiness and Maintenance Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shivam%20Dwivedi">Shivam Dwivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumit%20Prakash%20Gupta"> Sumit Prakash Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Durga%20Toshniwal"> Durga Toshniwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is a challenging task in today’s date to keep defence forces in the highest state of combat readiness with budgetary constraints. A huge amount of time and money is squandered in the unnecessary and expensive traditional maintenance activities. To overcome this limitation Defence Intelligent Mission Readiness and Maintenance Scheduling System has been proposed, which ameliorates the maintenance system by diagnosing the condition and predicting the maintenance requirements. Based on new data mining algorithms, this system intelligently optimises mission readiness for imminent operations and maintenance scheduling in repair echelons. With modified data mining algorithms such as Weighted Feature Ranking Genetic Algorithm and SVM-Random Forest Linear ensemble, it improves the reliability, availability and safety, alongside reducing maintenance cost and Equipment Out of Action (EOA) time. The results clearly conclude that the introduced algorithms have an edge over the conventional data mining algorithms. The system utilizing the intelligent condition-based maintenance approach improves the operational and maintenance decision strategy of the defence force. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition%20based%20maintenance" title="condition based maintenance">condition based maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=defence%20maintenance" title=" defence maintenance"> defence maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble" title=" ensemble"> ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenance%20scheduling" title=" maintenance scheduling"> maintenance scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=mission%20capability" title=" mission capability"> mission capability</a> </p> <a href="https://publications.waset.org/abstracts/73229/a-hybrid-data-mining-algorithm-based-system-for-intelligent-defence-mission-readiness-and-maintenance-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73229.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">297</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">4968</span> Genetic Algorithms Multi-Objective Model for Project Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elsheikh%20Asser">Elsheikh Asser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multi-objective model for project scheduling considering different scenarios such as least cost, least time, and target time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title="genetic algorithms">genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=time-cost%20trade-off" title=" time-cost trade-off"> time-cost trade-off</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20model" title=" multi-objective model"> multi-objective model</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20scheduling" title=" project scheduling"> project scheduling</a> </p> <a href="https://publications.waset.org/abstracts/16242/genetic-algorithms-multi-objective-model-for-project-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16242.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">413</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">4967</span> Solving Weighted Number of Operation Plus Processing Time Due-Date Assignment, Weighted Scheduling and Process Planning Integration Problem Using Genetic and Simulated Annealing Search Methods</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=Caner%20Erden"> Caner Erden</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumtaz%20Ipek"> Mumtaz Ipek</a>, <a href="https://publications.waset.org/abstracts/search?q=Ozer%20Uygun"> Ozer Uygun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied. <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=genetic%20search" title=" genetic search"> genetic search</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=hybrid%20meta-heuristics" title=" hybrid meta-heuristics"> hybrid meta-heuristics</a> </p> <a href="https://publications.waset.org/abstracts/57629/solving-weighted-number-of-operation-plus-processing-time-due-date-assignment-weighted-scheduling-and-process-planning-integration-problem-using-genetic-and-simulated-annealing-search-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57629.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">4966</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">4965</span> A Hybrid ICA-GA Algorithm for Solving Multiobjective Optimization of Production Planning Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Ramzi%20Jasim">Omar Ramzi Jasim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalal%20Sultan%20Ashour"> Jalal Sultan Ashour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problems in operation and it can potentially lead to poor customer satisfaction. In this paper, a hybrid evolutionary algorithm (ICA-GA) is presented, which integrates the merits of both imperialist competitive algorithm (ICA) and genetic algorithm (GA) for solving multi-objective MPS problems. In the presented algorithm, the colonies in each empire has be represented a small population and communicate with each other using genetic operators. By testing on 5 production scenarios, the numerical results of ICA-GA algorithm show the efficiency and capabilities of the hybrid algorithm in finding the optimum solutions. The ICA-GA solutions yield the lower inventory level and keep customer satisfaction high and the required overtime is also lower, compared with results of GA and SA in all production scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=master%20production%20scheduling" title="master production scheduling">master production scheduling</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=imperialist%20competitive%20algorithm" title=" imperialist competitive algorithm"> imperialist competitive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20algorithm" title=" hybrid algorithm"> hybrid algorithm</a> </p> <a href="https://publications.waset.org/abstracts/46493/a-hybrid-ica-ga-algorithm-for-solving-multiobjective-optimization-of-production-planning-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46493.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">4964</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">4963</span> Genetic Algorithms Based ACPS Safety</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emine%20Laarouchi">Emine Laarouchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniela%20Cancila"> Daniela Cancila</a>, <a href="https://publications.waset.org/abstracts/search?q=Laurent%20Soulier"> Laurent Soulier</a>, <a href="https://publications.waset.org/abstracts/search?q=Hakima%20Chaouchi"> Hakima Chaouchi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cyber-Physical Systems as drones proved their efficiency for supporting emergency applications. For these particular applications, travel time and autonomous navigation algorithms are of paramount importance, especially when missions are performed in urban environments with high obstacle density. In this context, however, safety properties are not properly addressed. Our ambition is to optimize the system safety level under autonomous navigation systems, by preserving performance of the CPS. At this aim, we introduce genetic algorithms in the autonomous navigation process of the drone to better infer its trajectory considering the possible obstacles. We first model the wished safety requirements through a cost function and then seek to optimize it though genetics algorithms (GA). The main advantage in the use of GA is to consider different parameters together, for example, the level of battery for navigation system selection. Our tests show that the GA introduction in the autonomous navigation systems minimize the risk of safety lossless. Finally, although our simulation has been tested for autonomous drones, our approach and results could be extended for other autonomous navigation systems such as autonomous cars, robots, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=safety" title="safety">safety</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicles" title=" unmanned aerial vehicles "> unmanned aerial vehicles </a>, <a href="https://publications.waset.org/abstracts/search?q=CPS" title=" CPS"> CPS</a>, <a href="https://publications.waset.org/abstracts/search?q=ACPS" title=" ACPS"> ACPS</a>, <a href="https://publications.waset.org/abstracts/search?q=drones" title=" drones"> drones</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</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/117828/genetic-algorithms-based-acps-safety" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117828.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">181</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">4962</span> Design and Optimization of Open Loop Supply Chain Distribution Network Using Hybrid K-Means Cluster Based Heuristic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Suresh">P. Suresh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Gunasekaran"> K. Gunasekaran</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Thanigaivelan"> R. Thanigaivelan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RFID" title="RFID">RFID</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20distribution%20network" title=" supply chain distribution network"> supply chain distribution network</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20loop%20supply%20chain" title=" open loop supply chain"> open loop supply chain</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=simulated%20annealing" title=" simulated annealing"> simulated annealing</a> </p> <a href="https://publications.waset.org/abstracts/110012/design-and-optimization-of-open-loop-supply-chain-distribution-network-using-hybrid-k-means-cluster-based-heuristic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110012.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">165</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">4961</span> Model Order Reduction Using Hybrid Genetic Algorithm and Simulated Annealing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Salah">Khaled Salah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model order reduction has been one of the most challenging topics in the past years. In this paper, a hybrid solution of genetic algorithm (GA) and simulated annealing algorithm (SA) are used to approximate high-order transfer functions (TFs) to lower-order TFs. In this approach, hybrid algorithm is applied to model order reduction putting in consideration improving accuracy and preserving the properties of the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original complex models being reduced. Compared to conventional mathematical methods that have been used to obtain a reduced order model of high order complex models, our proposed method provides better results in terms of reducing run-time. Thus, the proposed technique could be used in electronic design automation (EDA) tools. <p class="card-text"><strong>Keywords:</strong> <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=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20reduction" title=" model reduction"> model reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20function" title=" transfer function"> transfer function</a> </p> <a href="https://publications.waset.org/abstracts/97897/model-order-reduction-using-hybrid-genetic-algorithm-and-simulated-annealing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97897.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">143</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">4960</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">4959</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">4958</span> Genetic Algorithms for Feature Generation in the Context of Audio Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20A.%20Menezes">José A. Menezes</a>, <a href="https://publications.waset.org/abstracts/search?q=Giordano%20Cabral"> Giordano Cabral</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruno%20T.%20Gomes"> Bruno T. Gomes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20generation" title="feature generation">feature generation</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20learning" title=" feature learning"> feature 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=music%20information%20retrieval" title=" music information retrieval"> music information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/36638/genetic-algorithms-for-feature-generation-in-the-context-of-audio-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36638.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">434</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">4957</span> Hardware for Genetic Algorithm</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=Reza%20Tati"> Reza Tati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only do not this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hardware" title="hardware">hardware</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=computer%20science" title=" computer science"> computer science</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering" title=" engineering"> engineering</a> </p> <a href="https://publications.waset.org/abstracts/5598/hardware-for-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5598.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">506</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">4956</span> A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaohuizi%20Guo">Gaohuizi Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ning%20Zhang"> Ning Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA. <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=hybrid%20algorithm" title=" hybrid algorithm"> hybrid algorithm</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=sine%20cosine%20algorithm" title=" sine cosine algorithm"> sine cosine algorithm</a> </p> <a href="https://publications.waset.org/abstracts/129731/a-hybrid-multi-objective-firefly-sine-cosine-algorithm-for-multi-objective-optimization-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129731.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">168</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">4955</span> Chaos Fuzzy Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Jalali%20Varnamkhasti">Mohammad Jalali Varnamkhasti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The genetic algorithms have been very successful in handling difficult optimization problems. The fundamental problem in genetic algorithms is premature convergence. This paper, present a new fuzzy genetic algorithm based on chaotic values instead of the random values in genetic algorithm processes. In this algorithm, for initial population is used chaotic sequences and then a new sexual selection proposed for selection mechanism. In this technique, the population is divided such that the male and female would be selected in an alternate way. The layout of the male and female chromosomes in each generation is different. A female chromosome is selected by tournament selection size from the female group. Then, the male chromosome is selected, in order of preference based on the maximum Hamming distance between the male chromosome and the female chromosome or The highest fitness value of male chromosome (if more than one male chromosome is having the maximum Hamming distance existed), or Random selection. The selections of crossover and mutation operators are achieved by running the fuzzy logic controllers, the crossover and mutation probabilities are varied on the basis of the phenotype and genotype characteristics of the chromosome population. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving p-median problems published in the literature. <p class="card-text"><strong>Keywords:</strong> <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=fuzzy%20system" title=" fuzzy system"> fuzzy system</a>, <a href="https://publications.waset.org/abstracts/search?q=chaos" title=" chaos"> chaos</a>, <a href="https://publications.waset.org/abstracts/search?q=sexual%20selection" title=" sexual selection"> sexual selection</a> </p> <a href="https://publications.waset.org/abstracts/30310/chaos-fuzzy-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> 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