Hybridization of floating-point genetic algorithms using Hooke-Jeeves algorithm as an intelligent mutation operator

Mehmet Hakan Satman

Abstract


Hybridization of genetic algorithms increases the search capabilities by means of convergence rate and speed. In this paper, we suggest to use Hooke-Jeeves algorithm as a genetic operator which performs a local search using the best chromosome in a generation as the base point. As Hooke-Jeeves algorithm searches a subspace in all directions of parameters for a given starting point, it can be considered as an intelligent mutation operator, whereas, the classical mutation operator is totally blind. The operator is applied within a predefined probability. Simulation studies performed on optimizing some well-known set of test functions show that using such an intelligent mutation operator has significant effects even for small number of iterations.

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How to Cite this Article:

Mehmet Hakan Satman, Hybridization of floating-point genetic algorithms using Hooke-Jeeves algorithm as an intelligent mutation operator, Journal of Mathematical and Computational Science, Vol 5, No 3 (2015), 320-332

Copyright © 2015 Mehmet Hakan Satman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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