Genetic and Evolutionary Computation — Gecco 2004: Genetic and Evolutionary Computation Conference, Seattle, Wa, USA, June 26–30, 2004 Proceedings, Part II - Couverture souple

 
9783540223436: Genetic and Evolutionary Computation — Gecco 2004: Genetic and Evolutionary Computation Conference, Seattle, Wa, USA, June 26–30, 2004 Proceedings, Part II

Synopsis

MostMOEAsuseadistancemetricorothercrowdingmethodinobjectivespaceinorder to maintain diversity for the non-dominated solutions on the Pareto optimal front. By ensuring diversity among the non-dominated solutions, it is possible to choose from a variety of solutions when attempting to solve a speci?c problem at hand. Supposewehavetwoobjectivefunctionsf (x)andf (x).Inthiscasewecande?ne 1 2 thedistancemetricastheEuclideandistanceinobjectivespacebetweentwoneighboring individuals and we thus obtain a distance given by 2 2 2 d (x, x )=[f (x )?f (x )] +[f (x )?f (x )] . (1) 1 2 1 1 1 2 2 1 2 2 f wherex andx are two distinct individuals that are neighboring in objective space. If 1 2 2 2 the functions are badly scaled, e.g.[?f (x)] [?f (x)], the distance metric can be 1 2 approximated to 2 2 d (x, x )? [f (x )?f (x )] . (2) 1 2 1 1 1 2 f Insomecasesthisapproximationwillresultinanacceptablespreadofsolutionsalong the Pareto front, especially for small gradual slope changes as shown in the illustrated example in Fig. 1. 1.0 0.8 0.6 0.4 0.2 0 0 20 40 60 80 100 f 1 Fig.1.Forfrontswithsmallgradualslopechangesanacceptabledistributioncanbeobtainedeven if one of the objectives (in this casef ) is neglected from the distance calculations. 2 As can be seen in the ?gure, the distances marked by the arrows are not equal, but the solutions can still be seen to cover the front relatively well.

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9783540223443: Genetic and Evolutionary Computation — Gecco 2004: Genetic and Evolutionary Computation Conference Seattle, Wa, USA, June 26–30, 2004, Proceedings, Part I

Edition présentée

ISBN 10 :  3540223444 ISBN 13 :  9783540223443
Editeur : Springer-Verlag Berlin and Heide..., 2004
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