ACM Transactions on Mathematical Software (TOMS)
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Efficient differential evolution using speciation for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A sequential niche technique for multimodal function optimization
Evolutionary Computation
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This paper is to investigate the influence of a minimum population size on the performance of the species conservation technique in searching multiple solutions. The species conservation technique is combined a random search technique, which is a special genetic algorithm with one individual, to present an algorithm, called species conservation random search (SCRS), for solving multimodal problems. Each species is built around a dominating point, called the species seed, with a given species radius, and the species are saved in the species set. The random search is used to explore a new point in the neighborhood area of an initial point randomly selected from the species set. A modified species conservation technique has been developed to update species seeds according to these new exploration points. Numerical experiments demonstrate that the proposed SCRS is very effective in dealing with multimodal problems and can also find all the global solutions of test functions.