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Grid diversity operator for some population-based optimization algorithms.

Salah, Ahmed; Hart, Emma

Authors

Ahmed Salah



Abstract

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.

Presentation Conference Type Conference Paper (Published)
Conference Name GECCO’15 Companion
Start Date Jul 11, 2015
End Date Jul 15, 2015
Publication Date 2015
Deposit Date Oct 23, 2015
Publicly Available Date May 15, 2017
Peer Reviewed Peer Reviewed
Pages 1475-1476
Book Title Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15
ISBN 978-1-4503-3488-4
DOI https://doi.org/10.1145/2739482.2764664
Keywords Artificial Immune Systems; Evolutionary Algorithms; Optimization; Diversity; Grid;
Public URL http://researchrepository.napier.ac.uk/id/eprint/9210
Publisher URL http://dx.doi.org/10.1145/2739482.2764664
Contract Date May 15, 2017

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