Ahmed Salah
Grid diversity operator for some population-based optimization algorithms.
Salah, Ahmed; Hart, Emma
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.
Citation
Salah, A., & Hart, E. (2015). 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). https://doi.org/10.1145/2739482.2764664
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 |
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