分组方法必读文章

 

参考文献：

 

1 Variable Interaction Learning

 

@inproceedings{Chen:2010:LGO:1887255.1887289,
 author = {Chen, Wenxiang and Weise, Thomas and Yang, Zhenyu and Tang, Ke},
 title = {Large-scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning},
 booktitle = {Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part II},
 series = {PPSN'10},
 year = {2010},
 isbn = {3-642-15870-6, 978-3-642-15870-4},
 location = {Krak\&\#243;w, Poland},
 pages = {300--309},
 numpages = {10},
 url = {http://dl.acm.org/citation.cfm?id=1887255.1887289},
 acmid = {1887289},
 publisher = {Springer-Verlag},
 address = {Berlin, Heidelberg},
 keywords = {cooperative coevolution, incremental group strategy, large-scale optimization, numerical optimization, variable interaction learning},
}

W. Chen, T. Weise, Z. Yang, and K. Tang, “Large-scale global optimization using cooperative coevolution with variable interaction learning,” in International Conference on Parallel Problem Solving from Nature, 2010, pp. 300–309.

 

2 Differential Grouping

 

@ARTICLE{6595612, 
author={M. N. Omidvar and X. Li and Y. Mei and X. Yao}, 
journal={IEEE Transactions on Evolutionary Computation}, 
title={Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization}, 
year={2014}, 
volume={18}, 
number={3}, 
pages={378-393}, 
keywords={divide and conquer methods;evolutionary computation;automatic decomposition strategy;co-adapted subcomponents;cooperative co-evolution;decision variable interaction structure;differential grouping;divide-and-conquer paradigm;evolutionary algorithms;large-scale global optimization problems;near-optimal decomposition;partial separability;Context;Couplings;Evolutionary computation;Genetic algorithms;Linear programming;Optimization;Vectors;Cooperative co-evolution;cooperative co-evolution;large-scale optimization;non-separability;nonseparability;numerical optimization;problem decomposition}, 
doi={10.1109/TEVC.2013.2281543}, 
ISSN={1089-778X}, 

month={June},}

M. N. Omidvar, X. Li, Y. Mei, and X. Yao, “Cooperative co-evolution with differential grouping for large scale optimization,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 3, pp. 378–393, 2014.

 

3 Dependency Structure Matrix

 

@article{Yu:2009:DSM:1668000.1668011,
 author = {Yu, Tian-Li and Goldberg, David E. and Sastry, Kumara and Lima, Claudio F. and Pelikan, Martin},
 title = {Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination},
 journal = {Evol. Comput.},
 issue_date = {Winter 2009},
 volume = {17},
 number = {4},
 month = dec,
 year = {2009},
 issn = {1063-6560},
 pages = {595--626},
 numpages = {32},
 url = {http://dx.doi.org/10.1162/evco.2009.17.4.17409},
 doi = {10.1162/evco.2009.17.4.17409},
 acmid = {1668011},
 publisher = {MIT Press},
 address = {Cambridge, MA, USA},
 keywords = {Genetic algorithms, dependency structure matrix, hierarchy, modularity, overlap, problem decomposition},

} 

T. L. Yu, D. E. Goldberg, K. Sastry, C. F. Lima, and M. Pelikan, “Dependency structure matrix, genetic algorithms, and effective recombination,”Evolutionary Computation, vol. 17, no. 4, pp. 595–626, 2009.

 

4 Linkage Learning Genetic Algorithm

 

@article{Chen:2005:CTL:1109021.1109023,
 author = {Chen, Ying-Ping and Goldberg, David E.},
 title = {Convergence Time for the Linkage Learning Genetic Algorithm},
 journal = {Evol. Comput.},
 issue_date = {September 2005},
 volume = {13},
 number = {3},
 month = sep,
 year = {2005},
 issn = {1063-6560},
 pages = {279--302},
 numpages = {24},
 url = {http://dx.doi.org/10.1162/1063656054794806},
 doi = {10.1162/1063656054794806},
 acmid = {1109023},
 publisher = {MIT Press},
 address = {Cambridge, MA, USA},
} 

Y. P. Chen and D. E. Goldberg, “Convergence time for the linkage learning genetic algorithm,” Evolutionary Computation, vol. 13, no. 3, pp. 279–302, 2005.

 

5 Linkage Analysis in Genetic Algorithms

 

@Inbook{Tsuji2008,
author="Tsuji, Miwako
and Munetomo, Masaharu",
editor="Jain, Lakhmi C.
and Sato-Ilic, Mika
and Virvou, Maria
and Tsihrintzis, George A.
and Balas, Valentina Emilia
and Abeynayake, Canicious",
title="Linkage Analysis in Genetic Algorithms",
bookTitle="Computational Intelligence Paradigms: Innovative Applications",
year="2008",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="251--279",
abstract="A series of advanced techniques in genetic and evolutionary computation have been proposed that analyze gene linkage to realize competent genetic algorithms. Although it is important to encode linked variables tightly for simple GAs, it is sometimes difficult because it requires enough knowledge of problems to be solved. In order to solve real-world problems effectively even if the knowledge is not available, we need to analyze gene linkage.",
isbn="978-3-540-79474-5",
doi="10.1007/978-3-540-79474-5_12",
url="https://doi.org/10.1007/978-3-540-79474-5_12"
}

M. Tsuji and M. Munetomo, “Linkage analysis in genetic algorithms,” in Computational Intelligence Paradigms. Springer, 2008, pp. 251–279.

 

6 linkage identification by nonlinearity check

 

@INPROCEEDINGS{814159, 
author={M. Munetomo and D. E. Goldberg}, 
booktitle={Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on}, 
title={A genetic algorithm using linkage identification by nonlinearity check}, 
year={1999}, 
volume={1}, 
number={}, 
pages={595-600 vol.1}, 
keywords={algorithm theory;genetic algorithms;LINC procedure;LINC-GA;genetic algorithm;linkage identification;linkage identification by nonlinearity check;nonlinear functions;nonlinearity check;suboptimal solutions;Algorithm design and analysis;Buildings;Couplings;Data analysis;Data engineering;Encoding;Genetic algorithms;Genetic engineering;Laboratories;Transportation}, 
doi={10.1109/ICSMC.1999.814159}, 
ISSN={1062-922X}, 
month={},}

M. Munetomo and D. E. Goldberg, “A genetic algorithm using linkage identification by nonlinearity check,” in IEEE International Conference on Systems, Man, and Cybernetics, 1999, pp. 595–600.

 

7 Linkage Identification by Non-monotonicity Detection

 

@ARTICLE{6791515, 
author={M. Munetomo and D. E. Goldberg}, 
journal={Evolutionary Computation}, 
title={Linkage Identification by Non-monotonicity Detection for Overlapping Functions}, 
year={1999}, 
volume={7}, 
number={4}, 
pages={377-398}, 
keywords={Linkage identification;monotonicity detection;overlapping functions;population sizing}, 
doi={10.1162/evco.1999.7.4.377}, 
ISSN={1063-6560}, 
month={Dec},}

M. Munetomo and D. E. Goldberg, "Linkage Identification by Non-monotonicity Detection for Overlapping Functions," in Evolutionary Computation, vol. 7, no. 4, pp. 377-398, Dec. 1999.
doi: 10.1162/evco.1999.7.4.377
keywords: {Linkage identification;monotonicity detection;overlapping functions;population sizing},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6791515&isnumber=6791272

 

8 Linkage identification based on epistasis measures

 

@INPROCEEDINGS{1004436, 
author={M. Munetomo}, 
booktitle={Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on}, 
title={Linkage identification based on epistasis measures to realize efficient genetic algorithms}, 
year={2002}, 
volume={2}, 
number={}, 
pages={1332-1337}, 
keywords={genetic algorithms;identification;mathematical operators;LIEM;building blocks;genetic algorithms;genetic recombination operators;linkage group detection;linkage identification;pairwise epistasis measure;tightly linked loci;Couplings;Design optimization;Encoding;Gene expression;Genetic algorithms;Genetic mutations;History;Integrated circuit testing;Robustness}, 
doi={10.1109/CEC.2002.1004436}, 
ISSN={}, 
month={},}

M. Munetomo, "Linkage identification based on epistasis measures to realize efficient genetic algorithms," Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on, Honolulu, HI, 2002, pp. 1332-1337.
doi: 10.1109/CEC.2002.1004436
keywords: {genetic algorithms;identification;mathematical operators;LIEM;building blocks;genetic algorithms;genetic recombination operators;linkage group detection;linkage identification;pairwise epistasis measure;tightly linked loci;Couplings;Design optimization;Encoding;Gene expression;Genetic algorithms;Genetic mutations;History;Integrated circuit testing;Robustness},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1004436&isnumber=21687

 

9 Linkage identification with epistasis measures

 

——, “Linkage identification with epistasis measures considering monotonicity conditions,” in Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, 2002, pp. 231–238.

 

10 learning variable interdependencies

 

@INPROCEEDINGS{785469, 
author={K. Weicker and N. Weicker}, 
booktitle={Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)}, 
title={On the improvement of coevolutionary optimizers by learning variable interdependencies}, 
year={1999}, 
volume={3}, 
number={}, 
pages={1632 Vol. 3}, 
keywords={cooperative systems;evolutionary computation;learning (artificial intelligence);search problems;coevolutionary optimizers;common population;cooperating coevolutionary algorithms;cooperating coevolutionary function optimization;epistatic links;general applicability;interrelated dimensions;learning;optimization benchmarks;preliminary studies;search space;subpopulation;variable interdependencies;Collaboration;Computer science;Convergence;Couplings;Evolutionary computation;Genetic algorithms;Neural networks;Optimization methods;Thumb}, 
doi={10.1109/CEC.1999.785469}, 
ISSN={}, 
month={},}

K. Weicker and N. Weicker, "On the improvement of coevolutionary optimizers by learning variable interdependencies," Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, 1999, pp. 1632 Vol. 3.
doi: 10.1109/CEC.1999.785469
keywords: {cooperative systems;evolutionary computation;learning (artificial intelligence);search problems;coevolutionary optimizers;common population;cooperating coevolutionary algorithms;cooperating coevolutionary function optimization;epistatic links;general applicability;interrelated dimensions;learning;optimization benchmarks;preliminary studies;search space;subpopulation;variable interdependencies;Collaboration;Computer science;Convergence;Couplings;Evolutionary computation;Genetic algorithms;Neural networks;Optimization methods;Thumb},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=785469&isnumber=16970

 

11 Decision Variable Analyses

 

@ARTICLE{7155533, 
author={X. Ma and F. Liu and Y. Qi and X. Wang and L. Li and L. Jiao and M. Yin and M. Gong}, 
journal={IEEE Transactions on Evolutionary Computation}, 
title={A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables}, 
year={2016}, 
volume={20}, 
number={2}, 
pages={275-298}, 
keywords={decision theory;evolutionary computation;DVAs;MOEAs;MOPs;control variable analysis;cooperative coevolution;decision variable analysis;high-dimensional problem;interdependence variable analysis;large-scale variables;linkage learning methods;low-dimensional subproblems;multiobjective evolutionary algorithm;multiobjective optimization problems;objective functions;single objective optimization;Algorithm design and analysis;Buildings;Couplings;Evolutionary computation;Genetic algorithms;Linear programming;Optimization;Cooperative coevolution;Interacting variables;cooperative co-evolution;decision variable analysis (DVA);interacting variables;multiobjective optimization;problem decomposition}, 
doi={10.1109/TEVC.2015.2455812}, 
ISSN={1089-778X}, 
month={April},}

X. Ma et al., "A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables," in IEEE Transactions on Evolutionary Computation, vol. 20, no. 2, pp. 275-298, April 2016.
doi: 10.1109/TEVC.2015.2455812
keywords: {decision theory;evolutionary computation;DVAs;MOEAs;MOPs;control variable analysis;cooperative coevolution;decision variable analysis;high-dimensional problem;interdependence variable analysis;large-scale variables;linkage learning methods;low-dimensional subproblems;multiobjective evolutionary algorithm;multiobjective optimization problems;objective functions;single objective optimization;Algorithm design and analysis;Buildings;Couplings;Evolutionary computation;Genetic algorithms;Linear programming;Optimization;Cooperative coevolution;Interacting variables;cooperative co-evolution;decision variable analysis (DVA);interacting variables;multiobjective optimization;problem decomposition},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7155533&isnumber=7442915

 

12 Differential Evolution with Clustering Cooperative Coevolution

 

@INPROCEEDINGS{6973687, 
author={S. Wan}, 
booktitle={2013 International Conference on Information Science and Cloud Computing Companion}, 
title={Differential Evolution with Clustering Cooperative Coevolution for High-Dimensional Problems}, 
year={2013}, 
volume={}, 
number={}, 
pages={782-786}, 
keywords={evolutionary computation;pattern clustering;DE algorithm;clustering-cooperative coevolution scheme;differential evolution;evolutionary algorithm;high-dimensional optimization problems;Benchmark testing;Clustering algorithms;Correlation;Evolutionary computation;Heuristic algorithms;Optimization;Vectors;Differential Evolution;clustering cooperative coevolution;high-dimensional optimization problem}, 
doi={10.1109/ISCC-C.2013.64}, 
ISSN={}, 
month={Dec},}

S. Wan, "Differential Evolution with Clustering Cooperative Coevolution for High-Dimensional Problems," 2013 International Conference on Information Science and Cloud Computing Companion, Guangzhou, 2013, pp. 782-786.
doi: 10.1109/ISCC-C.2013.64
keywords: {evolutionary computation;pattern clustering;DE algorithm;clustering-cooperative coevolution scheme;differential evolution;evolutionary algorithm;high-dimensional optimization problems;Benchmark testing;Clustering algorithms;Correlation;Evolutionary computation;Heuristic algorithms;Optimization;Vectors;Differential Evolution;clustering cooperative coevolution;high-dimensional optimization problem},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6973687&isnumber=6973551

 

13 statistical variable interdependence learning

 

@article{SUN201220,
title = "A cooperative particle swarm optimizer with statistical variable interdependence learning",
journal = "Information Sciences",
volume = "186",
number = "1",
pages = "20 - 39",
year = "2012",
issn = "0020-0255",
doi = "https://doi.org/10.1016/j.ins.2011.09.033",
url = "http://www.sciencedirect.com/science/article/pii/S002002551100507X",
author = "Liang Sun and Shinichi Yoshida and Xiaochun Cheng and Yanchun Liang",
keywords = "Numerical optimization, Cooperative optimization, Variable interdependence, Problem decomposition"
}

Liang Sun, Shinichi Yoshida, Xiaochun Cheng, Yanchun Liang, A cooperative particle swarm optimizer with statistical variable interdependence learning, Information Sciences, Volume 186, Issue 1, 2012, Pages 20-39, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2011.09.033.
(http://www.sciencedirect.com/science/article/pii/S002002551100507X)

Keywords: Numerical optimization; Cooperative optimization; Variable interdependence; Problem decomposition

 

14 Linkage Identification by Nonlinearity Check

 

@InProceedings{10.1007/978-3-540-24855-2_20,
author="Tezuka, Masaru
and Munetomo, Masaharu
and Akama, Kiyoshi",
editor="Deb, Kalyanmoy",
title="Linkage Identification by Nonlinearity Check for Real-Coded Genetic Algorithms",
booktitle="Genetic and Evolutionary Computation -- GECCO 2004",
year="2004",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="222--233",
abstract="Linkage identification is a technique to recognize decomposable or quasi-decomposable sub-problems. Accurate linkage identification improves GA's search capability. We introduce a new linkage identification method for Real-Coded GAs called LINC-R (Linkage Identification by Nonlinearity Check for Real-Coded GAs). It tests nonlinearity by random perturbations on each locus in a real value domain. For the problem on which the proportion of nonlinear region in the domain is smaller, more perturbations are required to ensure LINC-R to detect nonlinearity successfully. If the proportion is known, the population size which ensures a certain success rate of LINC-R can be calculated. Computational experiments on benchmark problems showed that the GA with LINC-R outperforms conventional Real-Coded GAs and those with linkage identification by a correlation model.",
isbn="978-3-540-24855-2"

}

M. Tezuka, M. Munetomo, and K. Akama, “Linkage identification by nonlinearity check for real-coded genetic algorithms,” in The Genetic and Evolutionary Computation Conference, 2004, pp. 222–233.

 

15 Environment Sensitivity-based

 

@ARTICLE{7817874, 
 author={B. Xu and Y. Zhang and D. Gong and Y. Guo and M. Rong}, 
 journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics}, 
 title={Environment Sensitivity-based Cooperative Co-evolutionary Algorithms for Dynamic Multi-objective Optimization}, 
 year={2018}, 
 volume={PP}, 
 number={99}, 
 pages={1-1}, 
 keywords={Evolutionary computation;Heuristic algorithms;Optimization;Prediction algorithms;Sociology;Statistics;Vehicle dynamics;Dynamic multi-objective optimization;cooperative co-evolution;evolutionary algorithm;particle swarm}, 
 doi={10.1109/TCBB.2017.2652453}, 
 ISSN={1545-5963}, 
 month={},}

B. Xu, Y. Zhang, D. Gong, Y. Guo and M. Rong, "Environment Sensitivity-based Cooperative Co-evolutionary Algorithms for Dynamic Multi-objective Optimization," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. PP, no. 99, pp. 1-1.
doi: 10.1109/TCBB.2017.2652453
 keywords: {Evolutionary computation;Heuristic algorithms;Optimization;Prediction algorithms;Sociology;Statistics;Vehicle dynamics;Dynamic multi-objective optimization;cooperative co-evolution;evolutionary algorithm;particle swarm},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7817874&isnumber=4359833

 

16 Extended Differential Grouping

 

@inproceedings{Sun:2015:EDG:2739480.2754666,
 author = {Sun, Yuan and Kirley, Michael and Halgamuge, Saman Kumara},
 title = {Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions},
 booktitle = {Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation},
 series = {GECCO '15},
 year = {2015},
 isbn = {978-1-4503-3472-3},
 location = {Madrid, Spain},
 pages = {313--320},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/2739480.2754666},
 doi = {10.1145/2739480.2754666},
 acmid = {2754666},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {cooperative co-evolution, large scale global optimization, problem decomposition, variable interaction},
} 

Yuan Sun, Michael Kirley, and Saman Kumara Halgamuge. 2015. Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO '15), Sara Silva (Ed.). ACM, New York, NY, USA, 313-320. DOI: http://dx.doi.org/10.1145/2739480.2754666

 

17 DG2

 

@ARTICLE{7911173, 
author={M. N. Omidvar and M. Yang and Y. Mei and X. Li and X. Yao}, 
journal={IEEE Transactions on Evolutionary Computation}, 
title={DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization}, 
year={2017}, 
volume={21}, 
number={6}, 
pages={929-942}, 
keywords={divide and conquer methods;evolutionary computation;optimisation;sampling methods;DG2 algorithm;automatic threshold parameter calculation;cooperative co-evolutionary framework;differential grouping algorithm;divide-and-conquer algorithm;function evaluations;large-scale black-box optimization;overlapping components identification;roundoff errors;sampling technique;variable interaction identification;Benchmark testing;Computer science;Linear programming;Optimization;Reliability;Roundoff errors;Sensitivity;Cooperative co-evolution;differential grouping (DG);large-scale global optimization;problem decomposition}, 
doi={10.1109/TEVC.2017.2694221}, 
ISSN={1089-778X}, 
month={Dec},}

M. N. Omidvar, M. Yang, Y. Mei, X. Li and X. Yao, "DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization," in IEEE Transactions on Evolutionary Computation, vol. 21, no. 6, pp. 929-942, Dec. 2017.
doi: 10.1109/TEVC.2017.2694221
keywords: {divide and conquer methods;evolutionary computation;optimisation;sampling methods;DG2 algorithm;automatic threshold parameter calculation;cooperative co-evolutionary framework;differential grouping algorithm;divide-and-conquer algorithm;function evaluations;large-scale black-box optimization;overlapping components identification;roundoff errors;sampling technique;variable interaction identification;Benchmark testing;Computer science;Linear programming;Optimization;Reliability;Roundoff errors;Sensitivity;Cooperative co-evolution;differential grouping (DG);large-scale global optimization;problem decomposition},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7911173&isnumber=8124132

 

18 Cooperation Coevolution with Fast Interdependency Identification

 

@article{Hu:2017:CCF:3031116.3031343,
 author = {Hu, Xiao-Min and He, Fei-Long and Chen, Wei-Neng and Zhang, Jun},
 title = {Cooperation Coevolution with Fast Interdependency Identification for Large Scale Optimization},
 journal = {Inf. Sci.},
 issue_date = {March 2017},
 volume = {381},
 number = {C},
 month = mar,
 year = {2017},
 issn = {0020-0255},
 pages = {142--160},
 numpages = {19},
 url = {https://doi.org/10.1016/j.ins.2016.11.013},
 doi = {10.1016/j.ins.2016.11.013},
 acmid = {3031343},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 keywords = {Cooperative coevolution (CC), Differential evolution, Large scale global optimization (LSGO), Problem decomposition},
}

Xiao-Min Hu, Fei-Long He, Wei-Neng Chen, and Jun Zhang. 2017. Cooperation coevolution with fast interdependency identification for large scale optimization. Inf. Sci. 381, C (March 2017), 142-160. DOI: https://doi.org/10.1016/j.ins.2016.11.013

 

19 Quantifying Variable Interactions

 

@ARTICLE{7539575, 
author={Y. Sun and M. Kirley and S. K. Halgamuge}, 
journal={IEEE Transactions on Evolutionary Computation}, 
title={Quantifying Variable Interactions in Continuous Optimization Problems}, 
year={2017}, 
volume={21}, 
number={2}, 
pages={249-264}, 
keywords={combinatorial mathematics;decision theory;evolutionary computation;optimisation;ELA techniques;combinatorial problems;decision variables;evolutionary algorithm;exploratory landscape analysis;multidimensional continuous optimization functions;variable interactions;Algorithm design and analysis;Australia;Benchmark testing;Correlation;Microwave integrated circuits;Mutual information;Optimization;Continuous optimization problem;exploratory landscape analysis (ELA);maximal information coefficient (MIC);variable interaction}, 
doi={10.1109/TEVC.2016.2599164}, 
ISSN={1089-778X}, 
month={April},}

Y. Sun, M. Kirley and S. K. Halgamuge, "Quantifying Variable Interactions in Continuous Optimization Problems," in IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, pp. 249-264, April 2017.
doi: 10.1109/TEVC.2016.2599164
keywords: {combinatorial mathematics;decision theory;evolutionary computation;optimisation;ELA techniques;combinatorial problems;decision variables;evolutionary algorithm;exploratory landscape analysis;multidimensional continuous optimization functions;variable interactions;Algorithm design and analysis;Australia;Benchmark testing;Correlation;Microwave integrated circuits;Mutual information;Optimization;Continuous optimization problem;exploratory landscape analysis (ELA);maximal information coefficient (MIC);variable interaction},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7539575&isnumber=7889060

 

20 Fast Variable Interdependence Learning and Cross-cluster Mutation

 

@article{Ge:2015:CDE:2825275.2825440,
 author = {Ge, Hongwei and Sun, Liang and Yang, Xin and Yoshida, Shinichi and Liang, Yanchun},
 title = {Cooperative Differential Evolution with Fast Variable Interdependence Learning and Cross-cluster Mutation},
 journal = {Appl. Soft Comput.},
 issue_date = {November 2015},
 volume = {36},
 number = {C},
 month = nov,
 year = {2015},
 issn = {1568-4946},
 pages = {300--314},
 numpages = {15},
 url = {https://doi.org/10.1016/j.asoc.2015.07.016},
 doi = {10.1016/j.asoc.2015.07.016},
 acmid = {2825440},
 publisher = {Elsevier Science Publishers B. V.},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Cooperative optimization, Cross-cluster mutation, Differential evolution, Large scale optimization},
}

Hongwei Ge, Liang Sun, Xin Yang, Shinichi Yoshida, and Yanchun Liang. 2015. Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation. Appl. Soft Comput. 36, C (November 2015), 300-314. DOI: https://doi.org/10.1016/j.asoc.2015.07.016

 

21 Factored Evolutionary Algorithms

 

@ARTICLE{7548365, 
author={S. Strasser and J. Sheppard and N. Fortier and R. Goodman}, 
journal={IEEE Transactions on Evolutionary Computation}, 
title={Factored Evolutionary Algorithms}, 
year={2017}, 
volume={21}, 
number={2}, 
pages={281-293}, 
keywords={genetic algorithms;particle swarm optimisation;search problems;FEA algorithms;differential evolution;evolutionary search-based optimization algorithms;factor architecture determination;factored evolutionary algorithms;function variables subset;genetic algorithm;hill climbing algorithm;objective function;particle swarm optimization;Computer architecture;Evolutionary computation;Genetic algorithms;Inference algorithms;Neural networks;Open systems;Optimization;Differential evolution (DE);NK landscapes;genetic algorithm (GA);particle swarm optimization (PSO)}, 
doi={10.1109/TEVC.2016.2601922}, 
ISSN={1089-778X}, 
month={April},}

S. Strasser, J. Sheppard, N. Fortier and R. Goodman, "Factored Evolutionary Algorithms," in IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, pp. 281-293, April 2017.
doi: 10.1109/TEVC.2016.2601922
keywords: {genetic algorithms;particle swarm optimisation;search problems;FEA algorithms;differential evolution;evolutionary search-based optimization algorithms;factor architecture determination;factored evolutionary algorithms;function variables subset;genetic algorithm;hill climbing algorithm;objective function;particle swarm optimization;Computer architecture;Evolutionary computation;Genetic algorithms;Inference algorithms;Neural networks;Open systems;Optimization;Differential evolution (DE);NK landscapes;genetic algorithm (GA);particle swarm optimization (PSO)},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7548365&isnumber=7889060

 

22 Competitive-Cooperative Coevolutionary Paradigm

 

@ARTICLE{4553723, 
author={C. K. Goh and K. C. Tan}, 
journal={IEEE Transactions on Evolutionary Computation}, 
title={A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization}, 
year={2009}, 
volume={13}, 
number={1}, 
pages={103-127}, 
keywords={Pareto optimisation;evolutionary computation;Pareto front;competitive-cooperative coevolutionary paradigm;cooperation coevolutionary algorithm;dynamic multiobjective optimization;iterative process;Coevolution;dynamic multiobjective optimization;evolutionary algorithms}, 
doi={10.1109/TEVC.2008.920671}, 
ISSN={1089-778X}, 
month={Feb},}

C. K. Goh and K. C. Tan, "A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization," in IEEE Transactions on Evolutionary Computation, vol. 13, no. 1, pp. 103-127, Feb. 2009.
doi: 10.1109/TEVC.2008.920671
keywords: {Pareto optimisation;evolutionary computation;Pareto front;competitive-cooperative coevolutionary paradigm;cooperation coevolutionary algorithm;dynamic multiobjective optimization;iterative process;Coevolution;dynamic multiobjective optimization;evolutionary algorithms},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4553723&isnumber=4769002

 

23 competitive and cooperative co-evolutionary approach

 

@article{GOH201042,
title = "A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design",
journal = "European Journal of Operational Research",
volume = "202",
number = "1",
pages = "42 - 54",
year = "2010",
issn = "0377-2217",
doi = "https://doi.org/10.1016/j.ejor.2009.05.005",
url = "http://www.sciencedirect.com/science/article/pii/S0377221709003166",
author = "C.K. Goh and K.C. Tan and D.S. Liu and S.C. Chiam",
keywords = "Multi-objective optimization, Particle swarm optimization, Competitive鈥揷ooperative co-evolution"
}

C.K. Goh, K.C. Tan, D.S. Liu, S.C. Chiam,
A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design,
European Journal of Operational Research,
Volume 202, Issue 1,
2010,
Pages 42-54,
ISSN 0377-2217,
https://doi.org/10.1016/j.ejor.2009.05.005.
(http://www.sciencedirect.com/science/article/pii/S0377221709003166)

Keywords: Multi-objective optimization; Particle swarm optimization; Competitive–cooperative co-evolution