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A genetic algorithm for flowshop sequencing
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Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
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Applied Intelligence
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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IEEE Transactions on Evolutionary Computation
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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EuroGP'07 Proceedings of the 10th European conference on Genetic programming
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
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EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Using self-adaptable probes for dynamic parameter control of parallel evolutionary algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Evolutionary operator self-adaptation with diverse operators
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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In the majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has been argued that these settings should vary over the course of a genetic algorithm run---so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems and a hard problem from operations research, the flowshop sequencing problem, are therefore examined. The results obtained indicate that the applicability of operator adaptation is dependent upon three basic assumptions being satisfied by the problem being tackled.