When is an estimation of distribution algorithm better than an evolutionary algorithm?

Tianshi CHEN, Per Kristian LEHRE, Ke TANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

35 Citations (Scopus)

Abstract

Despite the wide-spread popularity of estimation of distribution algorithms (EDAs), there has been no theoretical proof that there exist optimisation problems where EDAs perform significantly better than traditional evolutionary algorithms. Here, it is proved rigorously that on a problem called SUBSTRING, a simple EDA called univariate marginal distribution algorithm (UMDA) is efficient, whereas the (1+1) EA is highly inefficient. Such studies are essential in gaining insight into fundamental research issues, i.e., what problem characteristics make an EDA or EA efficient, under what conditions an EDA is expected to outperform an EA, and what key factors are in an EDA that make it efficient or inefficient. © 2009 IEEE.
Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages1470-1477
Number of pages8
DOIs
Publication statusPublished - May 2009
Externally publishedYes

Fingerprint

Dive into the research topics of 'When is an estimation of distribution algorithm better than an evolutionary algorithm?'. Together they form a unique fingerprint.

Cite this