Improving genetic algorithms' performance by local search for continuous function optimization

HAMZAÇEBİ C.

APPLIED MATHEMATICS AND COMPUTATION, vol.196, no.1, pp.309-317, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 196 Issue: 1
  • Publication Date: 2008
  • Doi Number: 10.1016/j.amc.2007.05.068
  • Journal Name: APPLIED MATHEMATICS AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.309-317
  • Karadeniz Technical University Affiliated: Yes

Abstract

The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete functions problems. However, a simple GA may suffer from slow convergence, and instability of results. GAs' problem solution power can be increased by local searching. In this study a new local random search algorithm based on GAs is suggested in order to reach a quick and closer result to the optimum solution. (c) 2007 Elsevier Inc. All rights reserved.