Recombination and Self-Adaptation in Multi-objective Genetic Algorithms

Abstract

This paper investigates the influence of recombination and selfadaptation 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 selfadaptive recombination scheme is proposed to improve the robustness of MOGAs.

DOI: 10.1007/978-3-540-24621-3_10

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Cite this paper

@inproceedings{Sareni2003RecombinationAS, title={Recombination and Self-Adaptation in Multi-objective Genetic Algorithms}, author={Bruno Sareni and J{\'e}r{\'e}mi R{\'e}gnier and Xavier Roboam}, booktitle={Artificial Evolution}, year={2003} }