Massive parallelization of the compact genetic algorithm

Lobo, Fernando G.; Lima, Cláudio F.; Mártires, Hugo

doi:10.1007/3-211-27389-1_128

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…An interesting survey of PGA is published in [2]. An effective technique for the massive parallelization of compact GA was published in [12], [33]. An extremely prestigious PGA which is capable to solve billion-variable optimization problems was recently published in [7].…”

Section: Introductionmentioning

confidence: 99%

Parallel BMDA with an aggregation of probability models

Jaroš

Schwarz

2009

2009 IEEE Congress on Evolutionary Computation

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The paper is focused on the problem of aggregation of probability distribution applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new approach based on quantitative combination of probabilistic models is presented. Using this concept, the traditional migration of individuals is replaced with a newly proposed technique of probability parameter migration. In the proposed strategy, the adaptive learning of the resident probability model is used. The short theoretical study is completed by an experimental works for the implemented parallel BMDA algorithm (pBMDA). The performance of pBMDA algorithm is evaluated for various problem size (scalability) and interconnection topology. In addition, the comparison with the previously published aBMDA [24] is presented.

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Section: Introductionmentioning

confidence: 99%

Parallel BMDA with an aggregation of probability models

Jaroš

Schwarz

2009

2009 IEEE Congress on Evolutionary Computation

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“…An interesting survey of PGA is published in [2]. An effective technique for the massive parallelization of compact GA was published in [15]. An extremely prestigious PGA which is capable to solve billion-variable optimization problems was recently published in [10].…”

Section: Introductionmentioning

confidence: 99%

Parallel BMDA with probability model migration

Jaroš

Schwarz

2007

2007 IEEE Congress on Evolutionary Computation

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Summary. The chapter presents a new concept of parallel BivariateMarginal Distribution Algorithm (BMDA) using the stepping stone communication model with the unidirectional ring topology. The traditional migration of individuals is compared with a newly proposed technique of probability model migration. The idea of the new adaptive BMDA (aB-MDA) algorithms is to modify the classic learning of the probability model (applied in the sequential BMDA [24]). In the proposed strategy, the adaptive learning of the resident probability model is used. The evaluation of pair dependency, using Pearson's chi-square statistics is influenced by the relevant immigrant pair dependency according to the quality of resident and immigrant subpopulation. Experimental results show that the proposed adaptive aBMDA significantly outperforms the traditional concept of migration of individuals.

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“…The second example is that Lobo, Lima, and Martires [33]. They propose a parallel compact genetic algorithm using the master-slave model.…”

Section: Massive Parallelization Of the Compact Genetic Algorithmmentioning

confidence: 99%

Knowledge sharing in cooperative compact genetic algorithm

Gateratanakul

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The compact genetic algorithm is derived from the genetic algorithm in which the population is represented by the probabilistic vector.� To improve the search capability and avoiding local minima, parallelization has been employed where many search processes are deployed concurrently.� In order to coordinate the work of multiple processes, knowledge sharing is necessary.� Multiple processes share their probabilistic vectors partially. To escape from local minima the restart step is introduced.� The experiment compares the proposed algorithm with two other competitive algorithms using Traveling Salesman problem, Bin Packing problem, Subset Sum problem, and Knapsack problem. The results show that the proposed algorithm is more efficient in finding solutions than the competing algorithms.� The detailed analysis of the restart step provides insight into the behaviour of the proposed algorithm.

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Massive parallelization of the compact genetic algorithm

Cited by 7 publications

References 6 publications

Parallel BMDA with an aggregation of probability models

Parallel BMDA with an aggregation of probability models

Parallel BMDA with probability model migration

Knowledge sharing in cooperative compact genetic algorithm

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