Multi-modal search with convex bounding neighbourhood

Nguyen, D.H.M., Wong, K.P. and Chung, C. (2006) Multi-modal search with convex bounding neighbourhood. In: 2006 International Conference on Machine Learning and Cybernetics, 13 - 16 August 2006, Dalian, China pp. 2081-2086.

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

Item Type:	Conference Paper
Murdoch Affiliation(s):	School of Engineering Science
Notes:	Appears In: Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
URI:	http://researchrepository.murdoch.edu.au/id/eprint/12040

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