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|Title:||Runtime analyses for using fairness in evolutionary multi-objective optimization|
|Citation:||Parallel problem solving from nature - PPSN X : 10th International Conference, Dortmund, Germany, September 13-17, 2008 ; proceedings / Günter Rudolph ... [et al.] (eds.), pp.671-680|
|Series/Report no.:||Lecture Notes in Computer Science; 5199|
|Conference Name:||Conference on Parallel Problem Solving from Nature (10th : 2008 : Dortmund, Germany)|
|Tobias Friedrich, Christian Horoba, and Frank Neumann|
|Abstract:||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. . 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.|
|Description:||Also published as a journal article: Lecture notes in computer science, 2008; 5199:671-680|
|Rights:||© Springer-Verlag Berlin Heidelberg 2008|
|Appears in Collections:||Computer Science publications|
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