Evolving Mutation Rates for the Self-Optimisation of Genetic Algorithms

Authors:
Stevan Jay Anastasoff
Affiliations:
-
Venue:
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Year:
1999

Citing 5
Cited 0

Optimization of control parameters for genetic algorithms

IEEE Transactions on Systems, Man and Cybernetics
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Genetic Algorithms in Search, Optimization and Machine Learning
A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization

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An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.
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Abstract

A version of the standard genetic algorithm, in which the mutation rate is allowed to evolve freely, is applied across a set of optimisation problems. The resulting dynamics confirm the hypothesis that mutation rate, when allowed to evolve, will do so partly as a function of altitude in the fitness landscape. Further, it is demonstrated that this fact can be exploited in order to improve efficiency of the genetic algorithm when applied to a particular class of optimisation problem. Specifically, significant efficiency gains are established in those problems in which the fitness function is not stationary over time.