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Guided crossover: a new operator for genetic algorithm based optimization

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1 Author(s)
Rasheed, K. ; Dept. of Comput. Sci., Rutgers Univ., New Brunswick, NJ, USA

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

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Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on  (Volume:2 )

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