Quadratic Approximation-Based Coordinate Change in Genetic Algorithms

This paper proposes a procedure for space coordinate change, inside genetic algorithms, based on convex quadratic approximations of the general nonlinear objective function. It is shown that in the transformed coordinates the genetic algorithm is able to And the problem optimum in less iterations and with greater proportion of successful attempts. The proposed procedure employs only the objective function samples that have already been obtained through the usual genetic algorithm operations. It means that there is no need of any additional function evaluation. The proposed procedure was tested with a set of benchmark problems. In all cases, the proposed algorithm has been able to repeatedly find solutions closer to the true solution than those found by the same genetic algorithm without coordinate change. The results suggest that the modification can enhance the convergence rate and accuracy of genetic algorithms.