A Genetic Algorithm for Function Optimization

Abstract

A genetic algorithm implemented in Matlab is presented. Matlab is used for the following reasons: it provides many built in auxiliary functions useful for function optimization; it is completely portable; and it is eecient for numerical computations. The genetic algorithm toolbox developed is tested on a series of non-linear, multi-modal, non-convex test problems and compared with results using simulated annealing. The genetic algorithm using a aoat representation is found to be superior to both a binary genetic algorithm and simulated annealing in terms of eeciency and quality of solution. The use of genetic algorithm toolbox as well as the code is introduced in the paper.