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Diversity-Guided Evolutionary Algorithms
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Diversity-Guided Evolutionary Algorithms
Rasmus K. Ursem5 
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EVALife, Dept. of Computer Science, University of Aarhus, Bldg. 540 Ny Munkegade, DK-8000 Aarhus C, Denmark |
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.
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