Library Open Repository

Accelerating Real-Valued Genetic Algorithms Using Mutation-With-Momentum

Downloads

Downloads per month over past year

Temby, L and Vamplew, P and Berry, A (2005) Accelerating Real-Valued Genetic Algorithms Using Mutation-With-Momentum. Technical Report. Springer.

[img]
Preview
PDF
ai05-mumentum-long.pdf | Download (647kB)
Available under University of Tasmania Standard License.

Abstract

In a canonical genetic algorithm, the reproduction operators (crossover and mutation) are random in nature. The direction of the search carried out by the GA system is driven purely by the bias to fitter individuals in the selection process. Several authors have proposed the use of directed mutation operators as a means of improving the convergence speed of GAs on problems involving real-valued alleles. This paper proposes a new approach to directed mutation based on the momentum concept commonly used to accelerate the gradient descent training of neural networks. This mutation-with-momentum operator is compared against standard Gaussian mutation across a series of benchmark problems, and is shown to regularly result in rapid improvements in performance during the early generations of the GA. A hybrid system combining the momentum-based and standard mutation operators is shown to outperform either individual approach to mutation across all of the benchmarks.

Item Type: Report (Technical Report)
Keywords: Genetic algorithms, evolutionary computation, mutation operators, momentum
Publisher: Springer
Additional Information: This report is an extended version of a paper of the same title presented at AI'05: The 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 5-9 Dec 2005.The proceedings of this conference are published as part of the Springer Lecture Notes in Computer Science series, and are available from http://www.springeronline.com/lncs
Date Deposited: 15 Sep 2005
Last Modified: 18 Nov 2014 03:10
URI: http://eprints.utas.edu.au/id/eprint/198
Item Statistics: View statistics for this item

Repository Staff Only (login required)

Item Control Page Item Control Page