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Differential evolution with thresheld convergence
conference contribution
posted on 2023-05-23, 08:56 authored by Bolufe-Rohler, A, Estevez-Velarde, S, Piad-Morffis, A, Chen, S, Erin MontgomeryErin MontgomeryDuring 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.
History
Publication title
Proceedings of the 2013 IEEE Congress on Evolutionary ComputationPagination
40-47ISBN
978-1-4799-0453-2Department/School
School of Information and Communication TechnologyPublisher
IEEEPlace of publication
United States of AmericaEvent title
2013 IEEE Congress on Evolutionary ComputationEvent Venue
Cancun, MexicoDate of Event (Start Date)
2013-06-20Date of Event (End Date)
2013-06-23Rights statement
Copyright 2013 IEEERepository Status
- Restricted