A derivative-free algorithm for unconstrained optimization

In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm, but in the search step of pattern search algorithm, the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication, crossover and mutation, a finite set of points can be used. In theory, the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems, which other pattern search algorithms don’t bear. © 2005, Springer Verlag. All rights reserved.