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Genetic crossover operator for partially separable functions

Abstract : Partial separation is a mathematical technique that has been used in optimization for the last 15 years. On the other hand, genetic algorithms are widely used as global optimizers. This paper investigates how partial separability can be used in conjunction with GA. In the first part of this paper, a crossover operator designed to solve partially separable global optimization problems involving many variables is introduced. Then, a theoretical analysis is presented on a test case, along with practical experiments on fixed size populations, with different kinds of selection methods.
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Submitted on : Friday, April 25, 2014 - 3:12:24 PM
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  • HAL Id : hal-00937718, version 1



Nicolas Durand, Jean-Marc Alliot. Genetic crossover operator for partially separable functions. GP 1998, 3rd annual conference on Genetic Programming, Jul 1998, Madison, United States. pp xxxx. ⟨hal-00937718⟩



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