Hybridization of floating-point genetic algorithms using Hooke-Jeeves algorithm as an intelligent mutation operator
Mehmet Hakan Satman · Journal of Mathematical and Computational Science · 2015
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.