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Genetic diversity as an objective in multi-objective evolutionary algorithms

Published: 01 May 2003 Publication History

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 toplevel.

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Published In

cover image Evolutionary Computation
Evolutionary Computation  Volume 11, Issue 2
Summer 2003
94 pages
ISSN:1063-6560
EISSN:1530-9304
Issue’s Table of Contents

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 May 2003
Published in EVOL Volume 11, Issue 2

Author Tags

  1. Pareto optimality
  2. empirical comparison
  3. evolutionary algorithms
  4. genetic diversity
  5. multi-objective optimization

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