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. © Springer-Verlag 2004.
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CITATION STYLE
Sareni, B., Regnier, J., & Roboam, X. (2004). Recombination and Self-Adaptation in Multi-objective Genetic Algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2936, 115–126. https://doi.org/10.1007/978-3-540-24621-3_10
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