Guided Crossover: A New Operator for Genetic Algorithm Based Optimization

Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient-based methods which usually converge to local sub-optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations, whereas gradient-based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in several engineering design domains demonstrate that the operator can significantly improve the steady state error of the GA optimizer.