Coordinate change operators for genetic algorithms

Abstract

This paper studies the issue of space coordinate change in genetic algorithms, based on two methods: convex quadratic approximations, and principal component analysis. In both methods, the procedure employs only the objective function samples that have already been obtained through the usual genetic algorithm operations, without the need of any additional function evaluation. The two procedures have been tested over a set of benchmark problems, and the data has been analyzed via a stochastic dominance analysis procedure. In both cases, the results suggest that in the transformed coordinates the genetic algorithm can able to deal with ill-conditioned problems in less iterations and with greater proportion of successful attempts, in comparison to the genetic algorithm without coordinate transformation.