Share Email Print

Proceedings Paper

Extending self-adaptation in evolutionary algorithms
Author(s): David B. Fogel
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Self-adaptation in evolutionary algorithms concerns processes in which individuals incorporate information on how to search for new individuals. Instead of detailing the means for searching the space of possible solutions a priori, a process of random variation is applied both in terms of searching the space and searching for strategies to search the space. In one common implementation, each individual in the population is represented as a pair of vectors (x,σ), where x is the candidate solution to an optimization problem scored in terms of function f(x), and σ is the so-called strategy parameter vector that influences how offspring will be created from the individual. Typically, σ describes a variance or covariance matrix under Gaussian mutations. Experimental evidence suggest that the elements of σ can sometimes become too small to explore the given search space adequately. The evolutionary search then stagnates until the elements of σ grown sufficiently large as a result of random variation. Several methods have been offered to remedy this situation. This paper reviews recent results with one such method, which associates multiple strategy parameter vectors with a single individual. A single strategy vector is active at any time and dictates how offspring will be generated. Experiments on four 10-dimensional benchmark functions are reviewed, in which the number of strategy parameter vector is varied over 1, 2, 3, 4, 5, 10, and 20. The results indicate advantages for using multiple strategy parameter vectors. Furthermore, the relationship between the mean best result after a fixed number of generations and the number of strategy parameter vectors can be determined reliably in each case.

Paper Details

Date Published: 6 December 2002
PDF: 9 pages
Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); doi: 10.1117/12.455870
Show Author Affiliations
David B. Fogel, Natural Selection, Inc. (United States)

Published in SPIE Proceedings Vol. 4787:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?