Toward Comparison-based Adaptive Operator Selection

Álvaro Fialho 1 Marc Schoenauer 1, 2, 3 Michèle Sebag 1, 2, 3
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Adaptive Operator Selection (AOS) turns the impacts of the applications of variation operators into Operator Selection through a Credit Assignment mechanism. However, most Credit Assignment schemes make direct use of the fitness gain between parent and offspring. A first issue is that the Operator Selection technique that uses such kind of Credit Assignment is likely to be highly dependent on the a priori unknown bounds of the fitness function. Additionally, these bounds are likely to change along evolution, as fitness gains tend to get smaller as convergence occurs. Furthermore, and maybe more importantly, a fitness-based credit assignment forbid any invariance by monotonous transformation of the fitness, what is a usual source of robustness for comparison-based Evolutionary Algorithms. In this context, this paper proposes two new Credit Assignment mechanisms, one inspired by the Area Under the Curve paradigm, and the other close to the Sum of Ranks. Using fitness improvement as raw reward, and directly coupled to a Multi-Armed Bandit Operator Selection Rule, the resulting AOS obtain very good performances on both the OneMax problem and some artificial scenarios, while demonstrating their robustness with respect to hyper-parameter and fitness transformations. Furthermore, using fitness ranks as raw reward results in a fully comparison-based AOS with reasonable performances.
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Communication dans un congrès
Martin Pelikan and Jürgen Branke. Genetic and Evolutionary Computation Conference (GECCO 2010), Jul 2010, Portland, United States. ACM Press, pp.767-774, 2010
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Dernière modification le : jeudi 5 avril 2018 - 12:30:12
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Álvaro Fialho, Marc Schoenauer, Michèle Sebag. Toward Comparison-based Adaptive Operator Selection. Martin Pelikan and Jürgen Branke. Genetic and Evolutionary Computation Conference (GECCO 2010), Jul 2010, Portland, United States. ACM Press, pp.767-774, 2010. 〈inria-00471264v2〉

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