High performance parallel evolutionary algorithm model based on MapReduce framework

Du, Xueming; Ni, Youcong; Yao, Zhiqiang; Xiao, Ruliang; Xie, Dong

doi:10.1504/ijcat.2013.052807

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…While the above systems are examples of custom parallel GA implementations other recent parallel GA implementations are built on top of large-scale data processing frameworks. MR-GEP [10] is a scalable parallel evolutionary algorithm model based on MapReduce. Authors propose a hybrid model which consists of two layers.…”

Section: Related Workmentioning

confidence: 99%

Parallelism and partitioning in large-scale GAs using spark

Alterkawi

Migliavacca

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Section: Related Workmentioning

confidence: 99%

Parallelism and partitioning in large-scale GAs using spark

Alterkawi

Migliavacca

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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“…And also MPI is highly depending on the homogeneous hardware environment, which limits the algorithm adaption. Du et al also proposed a MapReduce [15] based distributed GEP algorithm to process large populations and datasets. Similar to [13], each Map computes the fitness and in each Reduce the selection, mutation, and crossover operations are executed.…”

Section: Related Workmentioning

confidence: 99%

Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

Huang

Shen

et al. 2017

Scientific Programming

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As one of the most effective function mining algorithms, Gene Expression Programming (GEP) algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.

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“…And also, MPI is highly depending on the homogeneous environment, which limits the algorithm adaption. Du et al also proposed a MapReduce‐based distributed GEP algorithm targeting on processing large populations and datasets. Similar to the research, each Map computes the fitness whilst in each Reduce the selection, mutation and crossover operations are executed.…”

Section: Related Workmentioning

confidence: 99%

MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

Liu

et al. 2017

Concurrency and Computation

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Summary Gene expression programming (GEP) algorithm is one of the most effective function mining algorithms in enabling the mathematical equation fitting for the input dataset. However, GEP algorithm encounters low efficiency issue in big data processing due to large overhead in its evolution when it handles the large‐scale data. In order to solve the issue, this paper presents two parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithm aims at speeding up the large‐scale classification. However, it is lack of ability to output the mined equation explicitly. Therefore, based on the further improvements of the first algorithm, the second parallelized GEP algorithm aims at mining the equation efficiently and also outputs the equation explicitly and directly. The experimental results show that both algorithms are effective for processing large volume of data.

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High performance parallel evolutionary algorithm model based on MapReduce framework

Cited by 17 publications

References 19 publications

Parallelism and partitioning in large-scale GAs using spark

Parallelism and partitioning in large-scale GAs using spark

Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

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