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Invited paper: a review of thresheld convergence


Chen, S and Montgomery, J and Bolufe-Rohler, A and Gonzalez-Fernandez, Y, Invited paper: a review of thresheld convergence, GECONTEC: Revista Internacional de Gestion del Conocimiento y la Tecnologia, 3, (1) pp. 1-13. ISSN 2255-5684 (2015) [Non Refereed Article]

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A multi-modal search space can be defined as having multiple attraction basins – each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers.

Item Details

Item Type:Non Refereed Article
Keywords:continuous optimisation, multi-modal search spaces, evolutionary computation
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:101015
Year Published:2015
Deposited By:Information and Communication Technology
Deposited On:2015-06-05
Last Modified:2015-06-05

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