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Genetic operators adapted to partially separable functions

Abstract : In this paper, a crossover operator for genetic algorithms is introduced to solve partially separable global optimization problems involving many variables. The fitness function must be an addition of positive sub-functions involving only a subset of the variables. A ''local fitness'' is associated to each variable and a parameter $\Delta$ controlling the operator's determinism is introduced. Combined with sharing and simulated annealing, this operator improves GAs efficiency to optimize combinational problems involving many variables. A polynomial function is given as an example and the operator is then used to solve a $200$ cities' TSP. The operator becomes necessary for problems such as conflict resolution involving many aircraft for air traffic control.
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Soumis le : vendredi 25 avril 2014 - 16:18:26
Dernière modification le : mardi 19 octobre 2021 - 11:02:49
Archivage à long terme le : : vendredi 25 juillet 2014 - 10:45:42


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  • HAL Id : hal-00940847, version 1



Nicolas Durand, Jean-Marc Alliot. Genetic operators adapted to partially separable functions. 1996. ⟨hal-00940847⟩



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