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## Fitness Uniform Deletion: A Simple Way to Preserve Diversity

Authors:Shane Legg and Marcus Hutter (2004-2005) Comments:8 two-column pages, 19 figures Subj-class:Neural and Evolutionary Computing; Artificial Intelligence Reference:Proc. Genetic and Evolutionary Computation Conference (GECCO 2005) pages ??-?? Report-no:IDSIA-11-04 and cs.LG/0403038 Paper:LaTeX - PostScript - PDF - Html/Gif Slides:PowerPoint - PDF C++ Code:FussDD.cpp - FussDD.h - FussTSP.cpp - FussTSP.h Review/Survey:in the Technology Reseach News Magazine (cached)

Keywords:Evolutionary algorithm, deletion schemes, fitness evaluation, optimization, fitness landscapes, (self)adaptation.

Abstract:A commonly experienced problem with population based optimisation methods is the gradual decline in population diversity that tends to occur over time. This can slow a system's progress or even halt it completely if the population converges on a local optimum from which it cannot escape. In this paper we present the Fitness Uniform Deletion Scheme (FUDS), a simple but somewhat unconventional approach to this problem. Under FUDS the deletion operation is modified to only delete those individuals which are "common" in the sense that there exist many other individuals of similar fitness in the population. This makes it impossible for the population to collapse to a collection of highly related individuals with similar fitness. Our experimental results on a range of optimisation problems confirm this, in particular for deceptive optimisation problems the performance is significantly more robust to variation in the selection intensity.

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@InProceedings{Hutter:05fuds, author = "S. Legg and M. Hutter", title = "Fitness Uniform Deletion for Robust Optimization", booktitle = "Proc. Genetic and Evolutionary Computation Conference ({GECCO-2005})", address = "Washington, OR", year = "2005", keywords = "Evolutionary algorithm, deletion schemes, fitness evaluation, optimization, fitness landscapes, (self)adaptation.", http = "http://www.hutter1.net/ai/fuds.htm", url = "http://arxiv.org/abs/cs.NE/0504035", ftp = "ftp://ftp.idsia.ch/pub/techrep/IDSIA-11-04.pdf", abstract = "A commonly experienced problem with population based optimisation methods is the gradual decline in population diversity that tends to occur over time. This can slow a system's progress or even halt it completely if the population converges on a local optimum from which it cannot escape. In this paper we present the Fitness Uniform Deletion Scheme (FUDS), a simple but somewhat unconventional approach to this problem. Under FUDS the deletion operation is modified to only delete those individuals which are ``common'' in the sense that there exist many other individuals of similar fitness in the population. This makes it impossible for the population to collapse to a collection of highly related individuals with similar fitness. Our experimental results on a range of optimisation problems confirm this, in particular for deceptive optimisation problems the performance is significantly more robust to variation in the selection intensity.", }

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