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Start Small, Grow Big? Saving Multi-objective Function Evaluations
Tobias Glasmachers1, Boris Naujoks2, and Günter Rudolph3
1Ruhr-Universität Bochum, Germany
tobias.glasmachers@ini.rub.de
2Cologne University of Applied Sciences, Germany
boris.naujoks@fh-koeln.de
3Technische Universität Dortmund, Germany
guenter.rudolph@tu-dortmund.de
Abstract. The influence of non-constant population sizes in evolutionary multi-objective optimization algorithms is investigated. In contrast to evolutionary single-objective optimization algorithms an increasing population size is considered beneficial when approaching the Pareto-front. Firstly, different deterministic schedules are tested, featuring different parameters like the initial population size. Secondly, a simple adaptation method is proposed. Considering all results, an increasing population size during an evolutionary multi-objective optimization algorithm run saves fitness function evaluations compared to a fixed population size. In particular, the results obtained with the adaptive method are most promising.
LNCS 8672, p. 579 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014