Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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Distributed genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Information Processing Letters
Shuffled complex evolution approach for effective and efficient global minimization
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Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
Local convergence rates of simple evolutionary algorithms withCauchy mutations
IEEE Transactions on Evolutionary Computation
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of convergence properties of a stochastic evolution algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Neural Networks
A Hybrid Particle Swarm Optimization for Manipulator Inverse Kinematics Control
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Applied Soft Computing
Engineering Applications of Artificial Intelligence
Genetic algorithms for video segmentation
Pattern Recognition
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
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Applied Soft Computing
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This paper presents a hybrid evolutionary search method based on clusters (HESC). The method is specifically designed to enhance the search efficiency while alleviating the problem of premature convergence inherent in standard evolutionary search methods (SES). It involves the simultaneous evolution of a main species and an additional fast mutating species. A hybrid search method which includes a local parallel single agent search and a global multiagent evolutionary search is carried out for the main species. Effective utilization of the search history is achieved with the clustering and training of a fuzzy ART neural network (ART NN) during the search. The advantages of HESC include 1) guaranteed population diversity at each generation, 2) effective integration of local search for the exploitation of important regions and the global search for the exploration of the entire space, and 3) fast exploration ability of the fast mutating species and migration from the additional species to the main species. Those advantages have been confirmed with experiments in which hard optimization problems were successfully solved with HESC.