The University of Southampton
University of Southampton Institutional Repository

Multi-level selection genetic algorithm applied to CEC '09 test instances

Multi-level selection genetic algorithm applied to CEC '09 test instances
Multi-level selection genetic algorithm applied to CEC '09 test instances

Genetic algorithms (GAs) are population-based optimisation tools inspired by evolution and natural selection. They are applied in many areas of engineering and industry, on increasingly complex problems. To improve the performance, the new algorithms have a tendency to be derived from sophisticated mathematical and computational mechanisms, where many biological and evolutionary advances have been neglected. One such mechanism is multi-level selection theory which has been proposed as being necessary for evolution. Previously, an algorithm developed using this theory as its inspiration has shown promising performance on simple test problems. It proposes the addition of a collective reproduction mechanism alongside the standard individual one. Here the algorithm, Multi-Level Selection Genetic Algorithm (MLSGA), is benchmarked on more sophisticated test instances from CEC '09 and compared to the final rankings. In this instance a simple genetic algorithm is used at the individual level. The developed algorithm cannot compete with top algorithms on complex unconstrained problems, however it shows interesting results and behaviour, and better performance on constrained test functions. The approach provides promise for further investigation, especially in integrating state-of-the-art individual reproduction methods to improve the performance and improving the novel collective mechanism.

Evolutionary computation, Evolutionary theory, Genetic algorithms, Multi-level selection, Multi-objective optimization
1613-1620
Institute of Electrical and Electronics Engineers Inc.
Grudniewski, Przemyslaw A.
31ca5517-c2c8-49dd-9536-6af3aefd8d33
Sobey, Adam J.
e850606f-aa79-4c99-8682-2cfffda3cd28
Grudniewski, Przemyslaw A.
31ca5517-c2c8-49dd-9536-6af3aefd8d33
Sobey, Adam J.
e850606f-aa79-4c99-8682-2cfffda3cd28

Grudniewski, Przemyslaw A. and Sobey, Adam J. (2017) Multi-level selection genetic algorithm applied to CEC '09 test instances. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. pp. 1613-1620 . (doi:10.1109/CEC.2017.7969495).

Record type: Conference or Workshop Item (Paper)

Abstract

Genetic algorithms (GAs) are population-based optimisation tools inspired by evolution and natural selection. They are applied in many areas of engineering and industry, on increasingly complex problems. To improve the performance, the new algorithms have a tendency to be derived from sophisticated mathematical and computational mechanisms, where many biological and evolutionary advances have been neglected. One such mechanism is multi-level selection theory which has been proposed as being necessary for evolution. Previously, an algorithm developed using this theory as its inspiration has shown promising performance on simple test problems. It proposes the addition of a collective reproduction mechanism alongside the standard individual one. Here the algorithm, Multi-Level Selection Genetic Algorithm (MLSGA), is benchmarked on more sophisticated test instances from CEC '09 and compared to the final rankings. In this instance a simple genetic algorithm is used at the individual level. The developed algorithm cannot compete with top algorithms on complex unconstrained problems, however it shows interesting results and behaviour, and better performance on constrained test functions. The approach provides promise for further investigation, especially in integrating state-of-the-art individual reproduction methods to improve the performance and improving the novel collective mechanism.

Full text not available from this repository.

More information

Published date: 5 July 2017
Venue - Dates: 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia-San Sebastian, Spain, 2017-06-05 - 2017-06-08
Keywords: Evolutionary computation, Evolutionary theory, Genetic algorithms, Multi-level selection, Multi-objective optimization

Identifiers

Local EPrints ID: 436329
URI: http://eprints.soton.ac.uk/id/eprint/436329
PURE UUID: b82757ad-8d9c-45cf-8157-37a7516d548a
ORCID for Przemyslaw A. Grudniewski: ORCID iD orcid.org/0000-0003-0635-3125
ORCID for Adam J. Sobey: ORCID iD orcid.org/0000-0001-6880-8338

Catalogue record

Date deposited: 06 Dec 2019 17:30
Last modified: 17 Dec 2019 01:44

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×