Dynamic optimization of migration topology in internet-based distributed genetic algorithms

Berntsson, Johan; Tang, Maolin

doi:10.1145/1068009.1068268

Cited by 6 publications

(10 citation statements)

References 3 publications

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“…The visualized results and performance of the experiments are shown by Figures 15,16,17,18,19,20,21,22,23 and 24.…”

Section: Parameter Optimization Resultsmentioning

confidence: 99%

“…The structure of the connections between islands, or the topology, is bounded by the cases of isolated islands (no migration) and fullyconnected (migration to all other demes) [22]. Additionally, dynamic topologies have been suggested [23]. In addition to the topology, the rate of migration, number of migrants, and the migration policy control the flow of individuals between islands [24].…”

Section: Distributed Evolutionmentioning

confidence: 99%

“…One aspect of a migration policy is whether the migration occurs synchronously (migrations occur in specific intervals with specific partners) or asynchronously (migrations occurred whenever a deme has a migrant to exchange) [25]. Interested readers are directed to [23,24,[26][27][28][29][30] for more detailed information regarding migration.…”

Section: Distributed Evolutionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Browne

Santos

2010

Applied Computational Intelligence and Soft Computing

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Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.

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“…The visualized results and performance of the experiments are shown by Figures 15,16,17,18,19,20,21,22,23 and 24.…”

Section: Parameter Optimization Resultsmentioning

confidence: 99%

Section: Distributed Evolutionmentioning

confidence: 99%

Section: Distributed Evolutionmentioning

confidence: 99%

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Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Browne

Santos

2010

Applied Computational Intelligence and Soft Computing

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“…In the experiments, the proposed approaches were compared with the topologies: Ring, Star, Random [7], Kmedoids [1] and Q-learning [3]. IM versions of the Differential Evolution Algorithm (DE) [6] using each one of the approaches were implemented.…”

Section: Experiments Settingmentioning

confidence: 99%

Dynamic selection of migration flows in island model differential evolution

Lopes

Silva

Campelo

et al. 2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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In this paper, a new approach to the topology configuration problem in the Island Model (IM) is proposed. The mechanism proposed works with a pool of candidates for migration and the choice of immigrants is performed using the usual selection techniques of evolutionary algorithms. Computational tests on IM versions of the Differential Evolution show positive effects of the proposed approach in terms of the number of function evaluations required for convergence.

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“…Although it is possible to find several different procedures to the configuration of the migratory flows, the majority of studies just compare them with traditional static configurations [4,30]. Therefore, the goal of this paper is to provide thorough experimental investigation of this procedures and foster the understanding of their impact in the context of Differential Evolution Algorithms.…”

Section: Introductionmentioning

confidence: 99%

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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Dynamic optimization of migration topology in internet-based distributed genetic algorithms

Cited by 6 publications

References 3 publications

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Dynamic selection of migration flows in island model differential evolution

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

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