Illustration of fairness in evolutionary multi-objective optimization |
| |
Authors: | Tobias Friedrich Frank Neumann |
| |
Affiliation: | a Max-Planck-Institut für Informatik, 66123, Saarbrücken, Germanyb Fakultät für Informatik, LS 2, TU Dortmund, 44221 Dortmund, Germany |
| |
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. 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 illustrative 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. |
| |
Keywords: | Evolutionary algorithms Fairness Multi-objective optimization Running time analysis Theory |
本文献已被 ScienceDirect 等数据库收录! |
|