Illustration of fairness in evolutionary multi-objective optimization
| Autor: | Christian Horoba, Frank Neumann, Tobias Friedrich |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
education.field_of_study
Mathematical optimization Speedup Optimization problem Fairness General Computer Science Mechanism (biology) Process (engineering) Population Evolutionary algorithm Running time analysis Evolutionary algorithms Multi-objective optimization Theoretical Computer Science Point (geometry) Theory education Mathematics Computer Science(all) |
| Zdroj: | Theoretical Computer Science. 412(17):1546-1556 |
| ISSN: | 0304-3975 |
| DOI: | 10.1016/j.tcs.2010.09.023 |
| 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. 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. |
| Databáze: | OpenAIRE |
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