Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

By Mohammad Nabi Omidvar, Xiaodong Li, Yi Mei, Xin Yao. IEEE Transactions on Evolutionary Computation, 2014

693 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ccd2e2cbd435e85d37398ae269e8ebd37edc6e93

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Citations in the corpus

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, methodology): “The CEC’2010 test suite has motivated the development of techniques such as random grouping [35], cooperative co-evolution [36], and differential grouping [49], which are used to detect interactions between groups of decision variables and solve them in a divide-and-conquer manner.”
Evolving Deep Convolutional Neural Networks for Image Classification (2017, background): “The main reason is the tremendousnumbers of weights, which are difﬁcult to be effectively encoded into the chromosomes and to be efﬁciently optimized due to its large-scale global optimization nature [42]. III. THE PROPOSED ALGORITHM In this section, the proposed Evolving deep Convolutional Neural N”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “We provide a two-tailed t-test among the results of DECC-DG and four algorithms MA-SW-CHAINS, MLCC, DECC-D, and DECC-DML on CEC’2010 benchmark test functions.”
Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II (2020, background): “EAs offer immense flexibility in accommodating diverse data representations (be it continuous or combinatorial [32]), and scale fairly well to higher dimensions [33], [34].”
A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “EMO methods do not scale well [210], [211], [244], [245].”
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (2017, methodology): “Differential grouping (DG) [32] is a competitive decomposition algorithm that can identify the nonseparable components of a continuous objective function and has shown superior performance as compared to other decomposition algorithms, such as variable interaction learning [33] on the CEC’2010 [50] ”
Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, methodology): “The decomposition approaches divide the decision variables into several groups and optimize each group of decision variables alternately, where several variable grouping strategies such as random grouping [195] and differential grouping [126] are adopted.”
Recent advances in differential evolution - An updated survey (2016, methodology): “[137] proposed a new technique to automatically decompose a problem based on the interdependencies of the decision variables.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “gest that large-scale global optimization [82] is challenging for MOPs.”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “[21], linkage detecting techniques are divided into four major categories: 1) perturbation; 2) interaction adaptation; 3) model building;”
A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “In [23], a mechanism called differential grouping (DG) was introduced to ﬁnd improved distributions of the variables in single-objective CC algorithms.”