Adaptive ε-Ranking on many-objective problems

This work proposes Adaptive ε-Ranking to enhance Pareto based selection, aiming to develop effective many -objective evolutionary optimization algorithms. ε-Ranking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure combined with ε...

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Veröffentlicht in:	Evolutionary intelligence Jg. 2; H. 4; S. 183 - 206
Hauptverfasser:	Aguirre, Hernán, Tanaka, Kiyoshi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.12.2009
Schlagworte:	Applications of Mathematics Artificial Intelligence Bioinformatics Control Engineering Mathematical and Computational Engineering Mechatronics Robotics Special Issue Statistical Physics and Dynamical Systems Epistasis Many-objective optimization Selection ε-Ranking Adaptation
ISSN:	1864-5909, 1864-5917
Online-Zugang:	Volltext
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