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Evol Comput. 2003 Summer;11(2):151-67.

Genetic diversity as an objective in multi-objective evolutionary algorithms.

Author information

1
Department of Mechanical Engineering, University of Padova Via Venezia 1, 35131 Padova, Italy. andrea.toffolo@unipd.it

Abstract

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

PMID:
12875667
DOI:
10.1162/106365603766646816
[Indexed for MEDLINE]

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