AbstractGenetic 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 two engineering design domains and across both binary and floating point representations demonstrate that the operator can significantly improve the steady state error of the GA optimizer.
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