Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed

Authors:
Álvaro Fialho;Marc Schoenauer;Michèle Sebag
Affiliations:
Microsoft Research - INRIA Joint Centre, Orsay, France;Microsoft Research - INRIA Joint Centre and Project-Team TAO, INRIA Saclay - Ile-de-France, Orsay, France;Microsoft Research - INRIA Joint Centre and Project-Team TAO, LRI (UMR CNRS 8623), Orsay, France
Venue:
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Year:
2010

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Abstract

The choice of which of the available strategies should be used within the Differential Evolution algorithm for a given problem is not trivial, besides being problem-dependent and very sensitive with relation to the algorithm performance. This decision can be made in an autonomous way, by the use of the Adaptive Strategy Selection paradigm, that continuously selects which strategy should be used for the next offspring generation, based on the performance achieved by each of the available ones on the current optimization process, i.e., while solving the problem. In this paper, we use the BBOB-2010 noiseless benchmarking suite to better empirically validate a comparison-based technique recently proposed to do so, the Fitness-based Area-Under-Curve Bandit [4], referred to as F-AUC-Bandit. It is compared with another recently proposed approach that uses Probability Matching technique based on the relative fitness improvements, referred to as PM-AdapSS-DE [7].