Research Output

Grid diversity operator for some population-based optimization algorithms.

  We present a novel diversity method named Grid Diversity
Operator (GDO) that can be incorporated into multiple
population-based optimization algorithms that guides the
containing algorithm in creating new individuals in sparsely
visited areas of the search space. Experimental tests on a set
of unimodal and multimodal benchmark functions from the
literature using GDO in conjunction with opt-aiNet algorithm
show that GDO maintains better diversity in most
cases, leading to an order-of-magnitude reduction in the
number of objective function evaluations needed to converge
while finding similar numbers of peaks in the majority of
benchmarks.

  • Publication Status:

    Published

  • DOI:

    10.1145/2739482.2764664

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Salah, A. & Hart, E. (2014). Grid diversity operator for some population-based optimization algorithms. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 1475-1476. doi:10.1145/2739482.2764664. ISBN 978-1-4503-3488-4

Authors

Keywords

Artificial Immune Systems; Evolutionary Algorithms; Optimization; Diversity; Grid;

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