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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.

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    Link to Published Version: http://dx.doi.org/10.1109/ICMLC.2006.258347
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    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

    Publication Type: Conference Paper
    Murdoch Affiliation: 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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