Skip to Main content Skip to Navigation
Other publications

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.
Document type :
Other publications
Complete list of metadata
Contributor : Laurence Porte Connect in order to contact the contributor
Submitted on : Friday, April 25, 2014 - 4:18:26 PM
Last modification on : Tuesday, October 19, 2021 - 11:02:49 AM
Long-term archiving on: : Friday, July 25, 2014 - 10:45:42 AM


Files produced by the author(s)


  • HAL Id : hal-00940847, version 1



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



Les métriques sont temporairement indisponibles