Accelerating real-valued genetic algorithms using mutation-with-momentum

Authors:
Luke Temby;Peter Vamplew;Adam Berry
Affiliations:
School of Computing, University of Tasmania, Hobart, Tasmania, Australia;School of Computing, University of Tasmania, Hobart, Tasmania, Australia;School of Computing, University of Tasmania, Hobart, Tasmania, Australia
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

Directed mutation has been proposed for improving the convergence speed of GAs on problems involving real-valued alleles. This paper proposes a directed mutation approach based on the momentum term used in gradient descent training of neural networks. Mutation-with-momentum is compared against gaussian mutation and is shown to regularly result in improvements in performance during early generations. A hybrid of momentum and gaussian mutation is shown to outperform either individual approach to mutation.