Improved S-CDAs using crossover controlling the number of crossed genes for many-objective optimization

@inproceedings{Sato2011ImprovedSU,
  title={Improved S-CDAs using crossover controlling the number of crossed genes for many-objective optimization},
  author={Hiroyuki Sato and Hern{\'a}n E. Aguirre and Kiyoshi Tanaka},
  booktitle={GECCO},
  year={2011}
}
Self-controlling dominance area of solutions (S-CDAS) reclassifies solutions in each front obtained by non-domination sorting to realize fine-grained ranking of solutions and improve the search performance of multi-objective evolutionary algorithms (MOEAs) in many-objective optimization problems (MaOPs). In this work, we further improve search performance of S-CDAS in MaOPs by analyzing genetic diversity in many-objective problems and enhancing crossover operators. First, we analyze genetic… CONTINUE READING
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