Managing Population Diversity Through the Use of Weighted Objectives and Modified Dominance: An Example from Data Mining

A. P. Reynolds, B. de la Iglesia

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

The most successful multi-objective metaheuristics, such as NSGA II and SPEA 2, usually apply a form of elitism in the search. However, there are multi-objective problems where this approach leads to a major loss of population diversity early in the search. In earlier work, the authors applied a multi-objective metaheuristic to the problem of rule induction for predictive classification, minimizing rule complexity and misclassification costs. While high quality results were obtained, this problem was found to suffer from such a loss of diversity. This paper describes the use of both linear combinations of objectives and modified dominance relations to control population diversity, producing higher quality results in shorter run times
Original languageEnglish
Pages99-106
Number of pages8
DOIs
Publication statusPublished - Apr 2007
EventProceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MCDM 2007) - Honolulu, HI
Duration: 1 Apr 20075 Apr 2007

Conference

ConferenceProceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MCDM 2007)
CityHonolulu, HI
Period1/04/075/04/07

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