Abstract
Genetic algorithm (GA) is an effective method of solving combinatorial optimization problems. Generally speaking most of search algorithms require a large execution time in order to calculate some evaluation value. Crossover is very important in GA because discovering a good solution efficiently requires that the good characteristics of the parent individuals be recombined. The Multiple Crossover Per Couple (MCPC) is a method that permits a variable number of children for each mating pair, and MCPC generates a huge search space. Thus this method requires a huge amount of execution time to find a good solution. This paper proposes a novel approach to reduce time needed for fitness evaluation by “prenatal diagnosis” using fitness prediction. In the experiments based on actual problems, the proposed method found an optimum solution 50% faster than the conventional method did. The experimental results from standard test functions show that the proposed method using the Distributed Genetic Algorithm is applicable to other problems as well.
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Mutoh, A., Nakamura, T., Kato, S., Itoh, H. (2003). A Proposal of an Efficient Crossover Using Fitness Prediction and Its Application. In: Gedeon, T.(.D., Fung, L.C.C. (eds) AI 2003: Advances in Artificial Intelligence. AI 2003. Lecture Notes in Computer Science(), vol 2903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24581-0_10
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DOI: https://doi.org/10.1007/978-3-540-24581-0_10
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