Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
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Genetic algorithms + data structures = evolution programs (3rd ed.)
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A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
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GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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Information Sciences: an International Journal
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Cybernetics and Systems Analysis
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This paper presents an evolutionary algorithm, which we call Evolutionary Algorithm with Species-specific Explosion (EASE), for multimodal optimization. EASE is built on the Species Conserving Genetic Algorithm (SCGA), and the design is improved in several ways. In particular, it not only identifies species seeds, but also exploits the species seeds to create multiple mutated copies in order to further converge to the respective optimum for each species. Experiments were conducted to compare EASE and SCGA on four benchmark functions. Cross-comparison with recent rival techniques on another five benchmark functions was also reported. The results reveal that EASE has a competitive edge over the other algorithms tested.