|Title:||Runtime analyses for using fairness in evolutionary multi-objective optimization|
|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 in [Laumanns, Thiele, Zitzler, IEEE TEC 2004]. This mechanism tries to balance the number of off-spring 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. We also indicate drawbacks for the use of fairness by presenting instances, where the optimization process is slowed down drastically.|
|Appears in Collections:||Sonderforschungsbereich (SFB) 531|
This item is protected by original copyright
If no CC-License is given, pleas contact the the creator, if you want to use thre resource other than only read it.