Parameter determination of induction machines by hybrid genetic algorithms

Authors:
Mümtaz Mutluer;Osman Bilgin;Mehmet Çunkas
Affiliations:
Department of Electrical and Electronics Engineering, Selçuk University, Konya, Turkey;Department of Electrical and Electronics Engineering, Selçuk University, Konya, Turkey;Department of Electronic and Computer Education, Selçuk University, Konya, Turkey
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Year:
2007

Citing 3
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Genetic Algorithms in Search, Optimization and Machine Learning

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Lamarckian Evolution, The Baldwin Effect and Function Optimization

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An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

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Abstract

In general, a genetic algorithm combined with other algorithms (e.g. tabu search, simulated annealing, etc.) is well known to be a powerful approach. In this paper, an efficient hybrid approach containing local search and genetic algorithms is presented. The purpose of the using local search mechanisms is to provide better the solution quality and to increase the convergence speed. It is demonstrated that the performance of the proposed algorithms is significantly better than the conventional genetic algorithm methods.