Performance of hybrid genetic algorithms incorporating local search

El-Mihoub, T., Hopgood, A.A., Nolle, L. and Battersby, A., 2004. Performance of hybrid genetic algorithms incorporating local search. In: Proceedings of the 18th European Simulation Multiconference. Erlangen, Germany: Gruner Druck, pp. 154-160. ISBN 3936150354

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Abstract

This paper investigates the effects of learning strategy and probability of local search on the performance of hybrid genetic algorithms. It compares the performance of two genetic-local hybrids using different learning strategies and different probabilities of local search. Two test functions are used for the comparisons. The results show that the solution quality of hybrids is not only affected by the Lamarckian or Baldwinian learning strategy, but also by the probability of local search. This probability, together with the learning strategy, has a great impact on population size requirements. These requirements are also affected by the local search method, and the fitness landscape. Reducing the population size can lead to an increase in the algorithm convergence speed

Item Type: Chapter in book
Creators: El-Mihoub, T., Hopgood, A.A., Nolle, L. and Battersby, A.
Publisher: Gruner Druck
Place of Publication: Erlangen, Germany
Date: 2004
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 09:44
Last Modified: 19 Oct 2015 14:22
URI: http://irep.ntu.ac.uk/id/eprint/1844

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