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Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES
Andrea Maesani and Dario Floreano
Laboratory of Intelligent Systems, Institute of Microengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerlandandrea.maesani@epfl.ch
dario.floreano@epfl.ch
http://lis.epfl.ch
Abstract. Viability Evolution is an abstraction of artificial evolution which operates by eliminating candidate solutions that do not satisfy viability criteria. Viability criteria are defined as boundaries on the values of objectives and constraints of the problem being solved. By adapting these boundaries it is possible to drive the search towards desired regions of solution space, discovering optimal solutions or those satisfying a set of constraints. Although in previous work we demonstrated the feasibility of the approach by implementing it on a simple genetic algorithm, the method was clearly not competitive with the current evolutionary computation state-of-the-art. In this work, we test Viability Evolution principles on a modified (1+1)-CMA-ES for constrained optimization. The resulting method shows competitive performance when tested on eight unimodal problems.
Keywords: Stochastic optimisation, constrained optimisation, evolution strategy, viability evolution, constraint handling
LNCS 8672, p. 272 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014