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Diversity-Guided Evolutionary Algorithms

  • Rasmus K. Ursem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few algorithms have used a measure to guide the search.

The diversity-guided evolutionary algorithm (DGEA) uses the wellknown distance-to-average-point measure to alternate between phases of exploration (mutation) and phases of exploitation (recombination and selection). The DGEA showed remarkable results on a set of widely used benchmark problems, not only in terms of fitness, but more important: The DGEA saved a substantial amount of fitness evaluations compared to the simple EA, which is a critical factor in many real-world applications.

Keywords

Genetic Algorithm Evolutionary Algorithm Local Optimum Premature Convergence Avalanche Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Rasmus K. Ursem
    • 1
  1. 1.EVALife, Dept. of Computer ScienceUniversity of AarhusAarhus CDenmark

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