Understanding the Treatment of Outliers in Multi-Objective Estimation of Distribution Algorithms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)


It has been already documented the fact that estimation of distribution algorithms suffer from loss of population diversity and improper treatment of isolated solutions. This situation is particularly severe in the case of multi-objective optimization, as the loss of solution diversity limits the capacity of an algorithm to explore the Pareto-optimal front at full extent.

A set of approaches has been proposed to deal with this problem but —to the best of our knowledge— there has not been a comprehensive comparative study on the outcome of those solutions and at what degree they actually solve the issue.

This paper puts forward such study by comparing how current approaches handle diversity loss when confronted to different multi-objective problems.


Multi-objective optimization Estimation of distribution algorithms Model building Outlier detection 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineeringPontifícia Universidade Católica do Rio de JaneiroRio de JaneiroBrazil
  2. 2.Instituto de Lógica, Filosofia e Teoria da Ciéncia (ILTC)NiteróiBrazil

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