Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 13, Issue 2, Pages 369-397Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2008.927707
Keywords
Genetic algorithms; optimization methods; search methods
Funding
- UNAM
- CONACYT
- PCIC-UNAM
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Using a set of different search metrics and a set of model landscapes we theoretically and empirically study how optimal mutation rates for the simple genetic algorithm (SGA) depend not only on the fitness landscape, but also on population size and population state. We discuss the limitations of current mutation rate heuristic, showing that any fixed mutation rate can be expected to be suboptimal in terms of balancing exploration and exploitation. We then develop a mutation rate heuristic that offers a better balance by assigning different mutation rates to different subpopulations. When the mutation rate is assigned through a ranking of the population, according to fitness for example, we call the resulting algorithm a Rank GA. We show how this Rank GA overcomes the limitations of other heuristics on a set of model problems showing under what circumstances it might be expected to outperform a SGA with any choice of mutation rate.
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