Dynamic selection of migration flows in island model differential evolution

Lopes, Rodolfo A.; Silva, Rodrigo C. P.; Campelo, Felipe; Guimarães, Frederico Gadelha

doi:10.1145/2464576.2464662

Cited by 6 publications

(4 citation statements)

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“…Following the same ideia, Lopes et al also proposed in 2013 [15], an identical mechanism using the well known Tournament selection algorithm. This selection method is a useful and robust selection approach commonly used by GAs [19].…”

Section: Configuration Using Tournamentmentioning

confidence: 99%

“…This approach was proposed by Lopes et al in 2013 [15], and it is similar to the mechanism of the Random configuration. However, in an attempt to explore the dynamics of the evolution of subpopulations, the selection methods proposed are based on the fitness of the individuals.…”

Section: Configuration Using Roulette Wheelmentioning

confidence: 99%

“…For the Tournament approach, the tournament size was defined as 3 and the parameter kt equal 1.0. The parameters of Tournament approach were defined according [15].…”

Section: Computational Experimentsmentioning

confidence: 99%

“…They are, Ring [6], Star [5], Random [30], K-medoids [4], Q-learning [16], Roulette Wheel [15] and Tournament [15]. The IM versions of the Differential Evolution Algorithm [28] were implemented and the experiments were conducted in the Large Scale Global Optimization benchmark.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A study on the configuration of migratory flows in island model differential evolution

Lopes

Silva

Freitas

et al. 2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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The Island Model (IM) is a well known multi-population approach for Evolutionary Algorithms (EAs). One of the critical parameters for defining a suitable IM is the migration topology. Basically it determines the Migratory Flows (MF) between the islands of the model which are able to improve the rate and pace of convergence observed in the EAs coupled with IMs. Although, it is possible to find a wide number of approaches for the configuration of MFs, there still is a lack of knowledge about the real performance of these approaches in the IM. In order to fill this gap, this paper presents a thorough experimental analysis of the approaches coupled with the state-of-the-art EA Differential Evolution. The experiments on well known benchmark functions show that there is a trade-off between convergence speed and convergence rate among the different approaches. With respect to the computational times, the results indicate that the increase in implementation complexity does not necessarily represent an increase in the overall execution time.

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Section: Configuration Using Tournamentmentioning

confidence: 99%

Section: Configuration Using Roulette Wheelmentioning

confidence: 99%

“…For the Tournament approach, the tournament size was defined as 3 and the parameter kt equal 1.0. The parameters of Tournament approach were defined according [15].…”

Section: Computational Experimentsmentioning

confidence: 99%