Cooperative Co-evolution with a new decomposition method for large-scale optimization

Mahdavi, Sedigheh; Shiri, Mohammad Ebrahim; Rahnamayan, Shahryar

doi:10.1109/cec.2014.6900327

Cited by 30 publications

(13 citation statements)

References 14 publications

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“…A metamodel-based decomposition method called high-dimensional model representation (HDMR) has also been proposed, which considered two variables non-separable when they have a cooperative effect on the approximated second-order HDMR model [42]. HDMR uses a map of linkage between input and output system variables.…”

Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 99%

Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

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A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity.

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Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 99%

Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

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“…Improved versions of random grouping and delta grouping use a so-called multilevel strategy [54,56] in which multiple fixed decompositions are used over the course of optimization. More sophisticated decomposition methods such as variable interaction learning [33], meta-modelling decomposition [40], statistical learning decomposition [38], and differential grouping [32] do not presuppose the number and/or size of components. Among these algorithms, differential grouping has shown superior performance with respect to grouping accuracy [33,40].…”

Section: Background and Related Workmentioning

confidence: 99%

“…More sophisticated decomposition methods such as variable interaction learning [33], meta-modelling decomposition [40], statistical learning decomposition [38], and differential grouping [32] do not presuppose the number and/or size of components. Among these algorithms, differential grouping has shown superior performance with respect to grouping accuracy [33,40]. The following theorem is at the heart of interaction detection of differential grouping: Theorem 1 ( [32]).…”

Section: Background and Related Workmentioning

confidence: 99%

“…A number of approaches such as dimensionality reduction [29], surrogate modelling [30], local search [21,22], and divide-and-conquer (a.k.a decomposition) methods [31] can be used for large-scale optimization, among which decomposition methods have gained popularity in recent years [18,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Decomposition methods break a large-scale problem into a set of smaller and simpler subproblems each of which is optimized in an iterative manner.…”

Section: Introductionmentioning

confidence: 99%

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DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization

Omidvar

Yang

Mei

et al. 2017

IEEE Trans. Evol. Computat.

273

179

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“…In CEC2014, a new decomposition method based on highdimensional model representation (HDMR) was proposed in [28]. It tries to identify the subcomponents before applying the optimization process.…”

Section: Related Workmentioning

confidence: 99%

LSHADE-SPA memetic framework for solving large-scale optimization problems

Hadi

Mohamed

Jambi

2018

Complex Intell. Syst.

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During the last decade, large-scale global optimization has been one of the active research fields. Optimization algorithms are affected by the curse of dimensionality associated with this kind of complex problems. To solve this problem, a new memetic framework for solving large-scale global optimization problems is proposed in this paper. In the proposed framework, success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration, while a modified version of multiple trajectory search is used for local exploitation. The framework introduced in this paper is further enhanced by the concept of divide and conquer, where the dimensions are randomly divided into groups, and each group is solved separately. The proposed framework is evaluated using IEEE CEC2010 and the IEEE CEC2013 benchmarks designed for large-scale global optimization. The comparison results between our framework and other state-of-the-art algorithms indicate that our proposed framework is competitive in solving large-scale global optimization problems.

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Cooperative Co-evolution with a new decomposition method for large-scale optimization

Cited by 30 publications

References 14 publications

Cooperative co-evolution for feature selection in Big Data with random feature grouping

Cooperative co-evolution for feature selection in Big Data with random feature grouping

DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization

LSHADE-SPA memetic framework for solving large-scale optimization problems

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