Using over-sampling in a bayesian classifier EDA to solve deceptive and hierarchical problems

David Wallin, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Evolutionary Algorithms based on Probabilistic Modeling is a growing research field. Recently, hybrids that borrow ideas from the field of classification were introduced. We extend such hybrids, and evaluate four strategies for truncation of an over-sized population of samples. The strategies are evaluated over a number of difficult problems from the literature, among them, a hierarchical 256-bit HIFF problem. We show that over-sampling in conjunction with a truncation strategy can guide the search without increasing the number of performed fitness evaluations per generation, and that a truncation strategy which inverses the sampling pressure can, fitness-wise, perform significantly better than regular sampling.

Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages1660-1667
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway
Duration: 18 May 200921 May 2009

Publication series

Name2009 IEEE Congress on Evolutionary Computation, CEC 2009

Conference

Conference2009 IEEE Congress on Evolutionary Computation, CEC 2009
Country/TerritoryNorway
CityTrondheim
Period18/05/0921/05/09

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