https://enac.hal.science/hal-00937718Durand, NicolasNicolasDurandENAC - Ecole Nationale de l'Aviation CivileAlliot, Jean-MarcJean-MarcAlliotENAC - Ecole Nationale de l'Aviation CivileGenetic crossover operator for partially separable functionsHAL CCSD1998[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Porte, Laurence2014-04-25 15:12:242021-10-19 11:02:492014-04-25 15:13:43enConference papersapplication/pdf1Partial 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.