Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
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Proceedings of the 3rd International Conference on Genetic Algorithms
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ICKS '08 Proceedings of the International Conference on Informatics Education and Research for Knowledge-Circulating Society (icks 2008)
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Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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On-line control of EA parameters is an approach to parameter setting that offers the advantage of values changing during the run. In this paper, we investigate parameter control from a generic and parameter-independent perspective. We propose a generic control mechanism that is targeted to repetitive applications, can be applied to any numeric parameter and is tailored to specific types of problems through an off-line calibration process. We present proof-of-concept experiments using this mechanism to control the mutation step size of an Evolutionary Strategy (ES). Results show that our method is viable and performs very well, compared to the tuning approach and traditional control methods.