Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization

Autor:	Christian Horoba, Tobias Friedrich, Frank Neumann
Rok vydání:	2008
Předmět:	education.field_of_study Mathematical optimization Optimization problem Speedup Computer science Process (engineering) Mechanism (biology) Population Evolutionary algorithm Point (geometry) education Multi-objective optimization
Zdroj:	Parallel Problem Solving from Nature – PPSN X ISBN: 9783540876991 PPSN
DOI:	10.1007/978-3-540-87700-4_67
Popis:	It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness introduced by Laumanns et al.[7]. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present showcase examples to point out situations where the right mechanism can speed up the optimization process significantly.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a0b7c74c513ab03dbd386c54440ca446 https://doi.org/10.1007/978-3-540-87700-4_67 Zobrazit plný text záznamu