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

HAMZAÇEBİ C.

APPLIED MATHEMATICS AND COMPUTATION, cilt.196, sa.1, ss.309-317, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 196 Sayı: 1
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.amc.2007.05.068
  • Dergi Adı: APPLIED MATHEMATICS AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.309-317
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.