Multi-modal search with convex bounding neighbourhood

D. H.M. Nguyen; K. P. Wong; C. Y. Chung

doi:10.1109/ICMLC.2006.258347

Multi-modal search with convex bounding neighbourhood

D. H.M. Nguyen, K. P. Wong, C. Y. Chung

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

This paper presents a new dynamic method of subpopulation in solving multi-modal search problems with evolutionary algorithms. The new method identify the modes found at each generation and equalises the subpopulation sizes assigned to each mode. Modes are identified sequentially starting with the highest fitness mode. Mode membership is determined by successive grouping of fitness dominated convex bounding neighbours, starting from the fittest individual. This new dynamic modal subpopulation approach is able to find a representative sample of optima for multi-modal landscape with infinite number of global and local optima with uneven heights and non-uniform distribution. The algorithm also facilitates parallel implementation.

Original language	English
Title of host publication	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages	2081-2086
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2006.258347
Publication status	Published - 2006
Event	2006 International Conference on Machine Learning and Cybernetics - Dalian, China Duration: 13 Aug 2006 → 16 Aug 2006

Publication series

Name	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Volume	2006

Conference

Conference	2006 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Dalian
Period	13/08/06 → 16/08/06

Keywords

Evolutionary computation
Multi-modal search
Parallel algorithm
Subpopulation techniques

ASJC Scopus subject areas

General Engineering

More information

10.1109/ICMLC.2006.258347

Cite this

Nguyen, D. H. M., Wong, K. P., & Chung, C. Y. (2006). Multi-modal search with convex bounding neighbourhood. In Proceedings of the 2006 International Conference on Machine Learning and Cybernetics (pp. 2081-2086). Article 4028407 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006). https://doi.org/10.1109/ICMLC.2006.258347

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title = "Multi-modal search with convex bounding neighbourhood",

abstract = "This paper presents a new dynamic method of subpopulation in solving multi-modal search problems with evolutionary algorithms. The new method identify the modes found at each generation and equalises the subpopulation sizes assigned to each mode. Modes are identified sequentially starting with the highest fitness mode. Mode membership is determined by successive grouping of fitness dominated convex bounding neighbours, starting from the fittest individual. This new dynamic modal subpopulation approach is able to find a representative sample of optima for multi-modal landscape with infinite number of global and local optima with uneven heights and non-uniform distribution. The algorithm also facilitates parallel implementation.",

keywords = "Evolutionary computation, Multi-modal search, Parallel algorithm, Subpopulation techniques",

author = "Nguyen, {D. H.M.} and Wong, {K. P.} and Chung, {C. Y.}",

year = "2006",

doi = "10.1109/ICMLC.2006.258347",

language = "English",

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series = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

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note = "2006 International Conference on Machine Learning and Cybernetics ; Conference date: 13-08-2006 Through 16-08-2006",

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Nguyen, DHM, Wong, KP & Chung, CY 2006, Multi-modal search with convex bounding neighbourhood. in Proceedings of the 2006 International Conference on Machine Learning and Cybernetics., 4028407, Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, vol. 2006, pp. 2081-2086, 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13/08/06. https://doi.org/10.1109/ICMLC.2006.258347

Multi-modal search with convex bounding neighbourhood. / Nguyen, D. H.M.; Wong, K. P.; Chung, C. Y.
Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. 2006. p. 2081-2086 4028407 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper presents a new dynamic method of subpopulation in solving multi-modal search problems with evolutionary algorithms. The new method identify the modes found at each generation and equalises the subpopulation sizes assigned to each mode. Modes are identified sequentially starting with the highest fitness mode. Mode membership is determined by successive grouping of fitness dominated convex bounding neighbours, starting from the fittest individual. This new dynamic modal subpopulation approach is able to find a representative sample of optima for multi-modal landscape with infinite number of global and local optima with uneven heights and non-uniform distribution. The algorithm also facilitates parallel implementation.

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Multi-modal search with convex bounding neighbourhood

Abstract

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Keywords

ASJC Scopus subject areas

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