This paper investigates the influence of recombination and self-adaptation in real-encoded Multi-Objective Genetic Algorithms (MOGAs). NSGA-II and SPEA2 are used as example to characterize the efficiency of MOGAs in relation to various recombination operators. The blend crossover, the simulated binary crossover and the breeder genetic crossover are compared for both MOGAs on multi-objective problems of the literature. Finally, a self-adaptive recombination scheme is proposed to improve the robustness of MOGAs.
Publié le : 2004-07-05
Classification:
Genetic algorithms,
Multiobjective optimization,
Self-adaptation,
Recombination operators,
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS],
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC],
[SPI.NRJ]Engineering Sciences [physics]/Electric power
@article{hal-00766887,
author = {Sareni, Bruno and Regnier, J\'er\'emi and Roboam, Xavier},
title = {Recombination and Self-Adaptation in Multi-objective Genetic Algorithms},
journal = {HAL},
volume = {2004},
number = {0},
year = {2004},
language = {en},
url = {http://dml.mathdoc.fr/item/hal-00766887}
}
Sareni, Bruno; Regnier, Jérémi; Roboam, Xavier. Recombination and Self-Adaptation in Multi-objective Genetic Algorithms. HAL, Tome 2004 (2004) no. 0, . http://gdmltest.u-ga.fr/item/hal-00766887/