Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Proceedings of the 3rd International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Artificial Evolution: A Continuing SAGA
ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Assortative mating drastically alters the magnitude of error thresholds
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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The error threshold -- a notion from molecular evolution -- is the critical mutation rate beyond which structures obtained by the evolutionary process are destroyed more frequently than selection can reproduce them. We argue that this notion is closely related to the more familiar notion of optimal mutation rates in Evolutionary Algorithms (EAs). This correspondence has been intuitively perceived before ([9], [11]). However, no previous study, to our knowledge, has been aimed at explicitly testing the hypothesis of such a relationship. Here we propose a methodology for doing so. Results on a restricted range of fitness landscapes suggest that these two notions are indeed correlated. There is not, however, a critically precise optimal mutation rate but rather a range of values producing similar near-optimal performance. When recombination is used, both error thresholds and optimal mutation ranges are lower than in the asexual case. This knowledge may have both theoretical relevance in understanding EA behavior, and practical implications for setting optimal values of evolutionary parameters.