Title

On The Performance Of Fitness Uniform Selection For Non-Deceptive Problems

Keywords

Fitness uniform selection; Genetic algorithms; Non-deceptive problems; Selection schemes

Abstract

Genetic algorithms (GAs) are probabilistic search techniques inspired by natural evolution. Selection schemes are used by GAs to choose individuals from a population to breed the next generation. Proportionate, ranking and tournament selection are standard selection schemes. They focus on choosing individuals with high fitness values. Fitness Uniform Selection Scheme (FUSS) is a recently proposed selection scheme that focuses on fitness diversity. FUSS have shown better performance than standard selection schemes for deceptive and NP-complete problems. In general, it is difficult to determine whether a real-life problem is deceptive or not. However, there is no information about the relative performance of FUSS on non-deceptive problems. In this paper, the standard selection schemes mentioned above were compared to FUSS on two non-deceptive problems. A GA using FUSS was able to find high-fitness solutions faster than expected. Consequently, FUSS could be a good first-choice selection scheme regardless of whether a problem at hand is deceptive or not. Copyright 2010 ACM.

Publication Date

12-1-2010

Publication Title

Proceedings of the Annual Southeast Conference

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1900008.1900053

Socpus ID

79951841937 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79951841937

This document is currently not available here.

Share

COinS