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Differential evolution with thresheld convergence


Bolufe-Rohler, A and Estevez-Velarde, S and Piad-Morffis, A and Chen, S and Montgomery, J, Differential evolution with thresheld convergence, Proceedings of the 2013 IEEE Congress on Evolutionary Computation, 20-23 June 2013, Cancun, Mexico, pp. 40-47. ISBN 978-1-4799-0453-2 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 IEEE

DOI: doi:10.1109/CEC.2013.6557551


During the search process of differential evolution (DE), each new solution may represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). This concurrent exploitation can interfere with exploration since the identification of a new more promising region depends on finding a (random) solution in that region which is better than its target solution. Ideally, every sampled solution will have the same relative fitness with respect to its nearby local optimum – finding the best region to exploit then becomes the problem of finding the best random solution. However, differential evolution is characterized by an initial period of exploration followed by rapid convergence. Once the population starts converging, the difference vectors become shorter, more exploitation is performed, and an accelerating convergence occurs. This rapid convergence can occur well before the algorithm’s budget of function evaluations is exhausted; that is, the algorithm can converge prematurely. In thresheld convergence, early exploitation is "held" back by a threshold function, allowing a longer exploration phase. This paper presents a new adaptive thresheld convergence mechanism which helps DE achieve large performance improvements in multi-modal search spaces.

Item Details

Item Type:Refereed Conference Paper
Keywords:thresheld convergence, differential evolution, exploration, exploitation, crowding, niching, multi-modal optimization
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:92138
Year Published:2013
Web of Science® Times Cited:14
Deposited By:Information and Communication Technology
Deposited On:2014-06-06
Last Modified:2017-11-20

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