CoChat
Sign Up
Cited by 270Metaheuristic Optimization Algorithms ResearchEvolutionary Algorithms and ApplicationsAdvanced Multi-Objective Optimization Algorithms

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Ensemble strategies for population-based optimization algorithms – A survey

Wu, Guohua, Mallipeddi, Rammohan, Suganthan, Ponnuthurai Nagaratnam·2019·Swarm and Evolutionary Computation
DOI 10.1016/j.swevo.2018.08.015
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.swevo.2018.08.015

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 150

  1. Hansen, 2003, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evol. Comput., 11, 1, 10.1162/106365603321828970 · DOI · Paper page
  2. Yao, 1999, Evolutionary programming made faster, IEEE Trans. Evol. Comput., 3, 82, 10.1109/4235.771163 · DOI
  3. Dorigo, 1996, Ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 26, 29, 10.1109/3477.484436 · DOI
  4. Karaboga, 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optim., 39, 459, 10.1007/s10898-007-9149-x · DOI
  5. Wu, 2016, Across neighborhood search for numerical optimization, Inf. Sci., 329, 597, 10.1016/j.ins.2015.09.051 · DOI
  6. Suganthan, 2005, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, KanGAL Rep., 2005005, 2005
  7. Tang, 2007, Benchmark functions for the CEC’2008 special session and competition on large scale global optimization, vol. 24
  8. Zhang, 2008, vol. 264
  9. Wu, 2013, A two-phase scheduling method with the consideration of task clustering for earth observing satellites, Comput. Oper. Res., 40, 1884, 10.1016/j.cor.2013.02.009 · DOI
  10. Bazi, 2014, Differential evolution extreme learning machine for the classification of hyperspectral images, Geosci. Rem. Sens. Lett. IEEE, 11, 1066, 10.1109/LGRS.2013.2286078 · DOI
  11. Bhadra, 2015, Unsupervised feature selection using an improved version of Differential Evolution, Expert Syst. Appl., 42, 4042, 10.1016/j.eswa.2014.12.010 · DOI
  12. Wu, 2016, Coordinated planning of heterogeneous earth observation resources, IEEE Trans. Syst. Man Cybern.: Systems, 46, 109, 10.1109/TSMC.2015.2431643 · DOI
  13. Chen, 2015, Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolution, Eur. J. Oper. Res., 242, 10, 10.1016/j.ejor.2014.09.025 · DOI · Paper page
  14. Hsieh, 2014, A PSO-based rule extractor for medical diagnosis, J. Biomed. Inf., 49, 53, 10.1016/j.jbi.2014.05.001 · DOI
  15. Gong, 2011, Adaptive strategy selection in differential evolution for numerical optimization: an empirical study, Inf. Sci., 181, 5364, 10.1016/j.ins.2011.07.049 · DOI
  16. Wu, 2016, Differential evolution with multi-population based ensemble of mutation strategies, Inf. Sci., 329, 329, 10.1016/j.ins.2015.09.009 · DOI · Paper page
  17. Zhao, 2012, Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, IEEE Trans. Evol. Comput., 16, 442, 10.1109/TEVC.2011.2166159 · DOI
  18. Mallipeddi, 2011, Differential evolution algorithm with ensemble of parameters and mutation strategies, Appl. Soft Comput., 11, 1679, 10.1016/j.asoc.2010.04.024 · DOI · Paper page
  19. Ren, 2016, Ensemble classification and regression-recent developments, applications and future directions, IEEE Comput. Intell. Mag., 11, 41, 10.1109/MCI.2015.2471235 · DOI
  20. Vrugt, 2009, Self-adaptive multimethod search for global optimization in real-parameter spaces, IEEE Trans. Evol. Comput., 13, 243, 10.1109/TEVC.2008.924428 · DOI
  21. Peng, 2010, Population-based algorithm portfolios for numerical optimization, IEEE Trans. Evol. Comput., 14, 782, 10.1109/TEVC.2010.2040183 · DOI
  22. Burke, 2013, Hyper-heuristics: a survey of the state of the art, J. Oper. Res. Soc., 64, 1695, 10.1057/jors.2013.71 · DOI
  23. Chen, 2011, A multi-facet survey on memetic computation, IEEE Trans. Evol. Comput., 15, 591, 10.1109/TEVC.2011.2132725 · DOI
  24. Moscato, 1989, vol. 826, 1989
  25. Wu, 2014, Superior solution guided particle swarm optimization combined with local search techniques, Expert Syst. Appl., 41, 7536, 10.1016/j.eswa.2014.06.005 · DOI
  26. Tang, 2014, Population-based Algorithm Portfolios with automated constituent algorithms selection, Inf. Sci., 279, 94, 10.1016/j.ins.2014.03.105 · DOI
  27. Qin, 2009, Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Trans. Evol. Comput., 13, 398, 10.1109/TEVC.2008.927706 · DOI
  28. Sarker, 2014, Differential evolution with dynamic parameters selection for optimization problems, IEEE Trans. Evol. Comput., 18, 689, 10.1109/TEVC.2013.2281528 · DOI
  29. Du, 2008, Multi-strategy ensemble particle swarm optimization for dynamic optimization, Inf. Sci., 178, 3096, 10.1016/j.ins.2008.01.020 · DOI
  30. Wang, 2011, Self-adaptive learning based particle swarm optimization, Inf. Sci., 181, 4515, 10.1016/j.ins.2010.07.013 · DOI
  31. Li, 2012, A self-learning particle swarm optimizer for global optimization problems, IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 42, 627, 10.1109/TSMCB.2011.2171946 · DOI
  32. Wang, 2014, Multi-strategy ensemble artificial bee colony algorithm, Inf. Sci., 279, 587, 10.1016/j.ins.2014.04.013 · DOI
  33. Wang, 2015, Multi-strategy ensemble artificial bee colony algorithm for large-scale production scheduling problem, Int. J. Innovat. Comput. Appl., 6, 128, 10.1504/IJICA.2015.072981 · DOI
  34. Xiong, 2013, Multi-strategy ensemble biogeography-based optimization for economic dispatch problems, Appl. Energy, 111, 801, 10.1016/j.apenergy.2013.04.095 · DOI
  35. Ma, 2015, Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling, Eng. Appl. Artif. Intell., 44, 79, 10.1016/j.engappai.2015.05.009 · DOI
  36. Gong, 2015, A multioperator search strategy based on cheap surrogate models for evolutionary optimization, IEEE Trans. Evol. Comput., 19, 746, 10.1109/TEVC.2015.2449293 · DOI
  37. Mallipeddi, 2010, Ensemble of constraint handling techniques, IEEE Trans. Evol. Comput., 14, 561, 10.1109/TEVC.2009.2033582 · DOI
  38. Elsayed, 2014, A self-adaptive combined strategies algorithm for constrained optimization using differential evolution, Appl. Math. Comput., 241, 267
  39. Elsayed, 2014, Self-adaptive mix of particle swarm methodologies for constrained optimization, Inf. Sci., 277, 216, 10.1016/j.ins.2014.01.051 · DOI
  40. Elsayed, 2015, Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization, Appl. Soft Comput., 26, 515, 10.1016/j.asoc.2014.10.011 · DOI
  41. Zhao, 2010, Multi-objective evolutionary algorithm with ensemble of external archives, Int. J. Innov. Comput. Inf. Control, 6, 1713
  42. Yen, 2014, Performance metric ensemble for multiobjective evolutionary algorithms, IEEE Trans. Evol. Comput., 18, 131, 10.1109/TEVC.2013.2240687 · DOI
  43. Wang, 2010, Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization, Memetic Comput., 2, 3, 10.1007/s12293-009-0012-0 · DOI
  44. Qu, 2012, Differential evolution with neighborhood mutation for multimodal optimization, IEEE Trans. Evol. Comput., 16, 601, 10.1109/TEVC.2011.2161873 · DOI
  45. Yu, 2010, Ensemble of niching algorithms, Inf. Sci., 180, 2815, 10.1016/j.ins.2010.04.008 · DOI
  46. Hui, 2016, Ensemble and arithmetic recombination-based speciation differential evolution for multimodal optimization, IEEE Trans. Cybern., 46, 64, 10.1109/TCYB.2015.2394466 · DOI
  47. Fan, 2015, Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization, Soft Comput., 19, 1363, 10.1007/s00500-014-1349-y · DOI
  48. Wang, 2011, Differential evolution with composite trial vector generation strategies and control parameters, IEEE Trans. Evol. Comput., 15, 55, 10.1109/TEVC.2010.2087271 · DOI
  49. Ali, 2015, Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization, Appl. Soft Comput., 33, 304, 10.1016/j.asoc.2015.04.019 · DOI
  50. Li, 2017, Multi-search differential evolution algorithm, Appl. Intell., 1
  51. Wang, 2016, Differential evolution optimization with time-frame strategy adaptation, Soft Comput., 1
  52. Lin, 2016, A novel adaptive control strategy for decomposition-based multiobjective algorithm, Comput. Oper. Res., 78, 94, 10.1016/j.cor.2016.08.012 · DOI
  53. Lin, 2016, Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm, Inf. Sci., 339, 332, 10.1016/j.ins.2015.12.022 · DOI
  54. Cui, 2016, Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations, Comput. Oper. Res., 67, 155, 10.1016/j.cor.2015.09.006 · DOI
  55. Li, 2016, Multi-population based ensemble mutation method for single objective bilevel optimization problem, IEEE Access., 4, 10.1109/ACCESS.2016.2617738 · DOI
  56. Debie, 2016, Investigating multi-operator differential evolution for feature selection, vol. 9592, 273
  57. Fan, 2016, Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies, IEEE Trans. Cybern., 46, 219, 10.1109/TCYB.2015.2399478 · DOI
  58. Yi, 2015, A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems, Appl. Intell., 42, 642, 10.1007/s10489-014-0620-3 · DOI
  59. Fan, 2016, Self-adaptive differential evolution algorithm with crossover strategies adaptation and its application in parameter estimation, Chemometr. Intell. Lab. Syst., 151, 164, 10.1016/j.chemolab.2015.12.020 · DOI
  60. Rakhshani, 2017, Intelligent multiple search strategy cuckoo algorithm for numerical and engineering optimization problems, Arabian J. Sci. Eng., 42, 567, 10.1007/s13369-016-2270-8 · DOI
  61. Wang, 2016, Self-adapting hybrid strategy particle swarm optimization algorithm, Soft Comput., 20, 4933, 10.1007/s00500-015-1784-4 · DOI
  62. Samma, 2016, A new reinforcement learning-based memetic particle swarm optimizer, Appl. Soft Comput., 43, 276, 10.1016/j.asoc.2016.01.006 · DOI
  63. Iacca, 2014, Multi-strategy coevolving aging particle optimization, Int. J. Neural Syst., 24, 10.1142/S0129065714500087 · DOI
  64. Gou, 2017, A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain, Neural Comput. Appl., 28, 1635, 10.1007/s00521-015-2134-2 · DOI
  65. Lin, 2016, Takagi-sugeno fuzzy model identification using coevolution particle swarm optimization with multi-strategy, Appl. Intell., 45, 187, 10.1007/s10489-015-0752-0 · DOI
  66. Tang, 2015, Multi-strategy adaptive particle swarm optimization for numerical optimization, Eng. Appl. Artif. Intell., 37, 9, 10.1016/j.engappai.2014.08.002 · DOI
  67. Xia, 2014, Combining crowding estimation in objective and decision space with multiple selection and search strategies for multi-objective evolutionary optimization, IEEE Trans. Cybern., 44, 378, 10.1109/TCYB.2013.2256418 · DOI
  68. Karafotias, 2015, Parameter control in evolutionary algorithms: trends and challenges, IEEE Trans. Evol. Comput., 19, 167, 10.1109/TEVC.2014.2308294 · DOI
  69. Qu, 2010, Dynamic grouping crowding differential evolution with ensemble of parameters for multi-modal optimization, vol. 6466, 19
  70. Iacca, 2014, A differential evolution framework with ensemble of parameters and strategies and pool of local search algorithms, vol. 8602, 615
  71. Zhang, 2009, JADE: adaptive differential evolution with optional external archive, IEEE Trans. Evol. Comput., 13, 945, 10.1109/TEVC.2009.2014613 · DOI
  72. Zhan, 2009, Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 39, 1362, 10.1109/TSMCB.2009.2015956 · DOI
  73. Brest, 2006, Self-Adapting control parameters in differential evolution: a comparative study on numerical benchmark problems, IEEE Trans. Evol. Comput., 10, 646, 10.1109/TEVC.2006.872133 · DOI
  74. Karthikeyan, 2015, Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks, Soft Comput., 19, 489, 10.1007/s00500-014-1269-x · DOI
  75. Xue, 2014, An ensemble algorithm with self-adaptive learning techniques for high-dimensional numerical optimization, Appl. Math. Comput., 231, 329
  76. Elsayed, 2013, Self-adaptive differential evolution incorporating a heuristic mixing of operators, Comput. Optim. Appl., 54, 771, 10.1007/s10589-012-9493-8 · DOI
  77. J. Brest, B. Bošković, A. Zamuda, I. Fister, and E. Mezura-Montes, "Real Parameter Single Objective Optimization Using Self-adaptive Differential Evolution Algorithm with More Strategies," pp. 377-383. · DOI
  78. Zhong, 2013, A differential evolution algorithm with dual populations for solving periodic railway timetable scheduling problem, IEEE Trans. Evol. Comput., 17, 512, 10.1109/TEVC.2012.2206394 · DOI
  79. Piotrowski, 2013, Adaptive memetic differential evolution with global and local neighborhood-based mutation operators, Inf. Sci., 241, 164, 10.1016/j.ins.2013.03.060 · DOI
  80. Cheng, 2015, Multicriteria adaptive differential evolution for global numerical optimization, Integrated Comput. Aided Eng., 22, 103, 10.3233/ICA-150481 · DOI
  81. T. J. Choi and C. W. Ahn, "An Adaptive Cauchy Differential Evolution Algorithm with Population Size Reduction and Modified Multiple Mutation Strategies," pp. 13-26. · DOI
  82. Xiang, 2015, An enhanced differential evolution algorithm based on multiple mutation strategies, Comput. Intell. Neurosci., 2015, 10.1155/2015/285730 · DOI
  83. Lu, 2014, A new self-adaptation scheme for differential evolution, Neurocomputing, 146, 2, 10.1016/j.neucom.2014.04.071 · DOI
  84. Tang, 2015, Differential evolution with an individual-dependent mechanism, IEEE Trans. Evol. Comput., 19, 560, 10.1109/TEVC.2014.2360890 · DOI
  85. Zhou, 2014, Enhancing differential evolution with role assignment scheme, Soft Comput., 18, 2209, 10.1007/s00500-013-1195-3 · DOI
  86. Zhang, 2017, An efficient multiple variants coordination framework for differential evolution, IEEE Trans. Cybern., 47, 2780, 10.1109/TCYB.2017.2712738 · DOI
  87. Li, 2016, A novel hybrid differential evolution algorithm with modified CoDE and JADE, Appl. Soft Comput., 47, 577, 10.1016/j.asoc.2016.06.011 · DOI
  88. Thangavelu, 2015, An investigation on mixing heterogeneous differential evolution variants in a distributed framework, Int. J. Bio-Inspired Comput., 7, 307, 10.1504/IJBIC.2015.072259 · DOI
  89. Wu, 2018, Ensemble of differential evolution variants, Inf. Sci., 423, 172, 10.1016/j.ins.2017.09.053 · DOI · Paper page
  90. Lynn, 2017, Ensemble particle swarm optimizer, Appl. Soft Comput., 55, 533, 10.1016/j.asoc.2017.02.007 · DOI
  91. Yuen, 2015, On composing an algorithm portfolio, Memetic Comput., 7, 203, 10.1007/s12293-015-0159-9 · DOI
  92. Elsayed, 2013, Adaptive Configuration of evolutionary algorithms for constrained optimization, Appl. Math. Comput., 222, 680
  93. Hongxin, 2014, Adaptive multiple evolutionary algorithms search for multi-objective optimal reactive power dispatch, Int. Trans. Electr. Energy Syst., 24, 780, 10.1002/etep.1730 · DOI
  94. Xie, 2013, A multi-algorithm balancing convergence and diversity for multi-objective optimization, J. Inf. Sci. Eng., 29, 811
  95. Zhang, 2010, On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model, Hydrol. Process., 24, 955, 10.1002/hyp.7528 · DOI
  96. Vrugt, 2007, Improved evolutionary optimization from genetically adaptive multimethod search, Proc. Natl. Acad. Sci. Unit. States Am., 104, 708, 10.1073/pnas.0610471104 · DOI
  97. Mashwani, 2016, Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation, Appl. Soft Comput., 39, 292, 10.1016/j.asoc.2015.08.059 · DOI
  98. Akay, 2017, Parallel population-based algorithm portfolios: an empirical study, Neurocomputing, 247, 115, 10.1016/j.neucom.2017.03.061 · DOI
  99. Mallipeddi, 2009, Differential evolution algorithm with ensemble of populations for global numerical optimization, OPSEARCH, 46, 184, 10.1007/s12597-009-0012-3 · DOI
  100. Gao, 2015, A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion, Expert Syst. Appl., 42, 7652, 10.1016/j.eswa.2015.06.004 · DOI
  101. Yoon, 2002, An empirical study on the synergy of multiple crossover operators, IEEE Trans. Evol. Comput., 6, 212, 10.1109/4235.996022 · DOI
  102. LaTorre, 2011, A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test, Soft Comput., 15, 2187, 10.1007/s00500-010-0646-3 · DOI
  103. Goel, 2007, Ensemble of surrogates, Struct. Multidiscip. Optim., 33, 199, 10.1007/s00158-006-0051-9 · DOI
  104. Mallipeddi, 2015, An evolving surrogate model-based differential evolution algorithm, Appl. Soft Comput., 34, 770, 10.1016/j.asoc.2015.06.010 · DOI
  105. Elsayed, 2011, Multi-operator based evolutionary algorithms for solving constrained optimization problems, Comput. Oper. Res., 38, 1877, 10.1016/j.cor.2011.03.003 · DOI
  106. Lynn, 2015, Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation, Swarm Evol. Comput., 24, 11, 10.1016/j.swevo.2015.05.002 · DOI
  107. Tasgetiren, 2010, An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem, Appl. Math. Comput., 215, 3356
  108. Mallipeddi, 2010, Ensemble strategies with adaptive evolutionary programming, Inf. Sci., 180, 1571, 10.1016/j.ins.2010.01.007 · DOI
  109. Mallipeddi, 2010, Differential evolution algorithm with ensemble of parameters and mutation and crossover strategies, vol. 6466, 71
  110. Pan, 2011, A differential evolution algorithm with self-adapting strategy and control parameters, Comput. Oper. Res., 38, 394, 10.1016/j.cor.2010.06.007 · DOI
  111. Zamuda, 2015, Self-adaptive control parameters׳ randomization frequency and propagations in differential evolution, Swarm Evol. Comput., 25, 72, 10.1016/j.swevo.2015.10.007 · DOI
  112. Li, 2016, Multi-population based ensemble mutation method for single objective bilevel optimization problem, IEEE Access., 4, 7262, 10.1109/ACCESS.2016.2617738 · DOI
  113. Wang, 2015, Multi-strategy ensemble artificial bee colony algorithm for large-scale production scheduling problem, Int. J. Innovat. Comput. Appl., 6, 128, 10.1504/IJICA.2015.072981 · DOI
  114. Qu, 2012, Ensemble of clearing differential evolution for multi-modal optimization, vol. 7331, 350
  115. Qu, 2010, Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods, Eng. Optim., 43, 403, 10.1080/0305215X.2010.493937 · DOI
  116. Li, 2011, Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems, Int. J. Adv. Manuf. Technol., 55, 1159, 10.1007/s00170-010-3140-2 · DOI
  117. Gao, 2015, Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion, Comput. Ind. Eng., 90, 107, 10.1016/j.cie.2015.09.005 · DOI
  118. Jin, 2001, Comparative studies of metamodelling techniques under multiple modelling criteria, Struct. Multidiscip. Optim., 23, 1, 10.1007/s00158-001-0160-4 · DOI
  119. Burke, 2010, A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics, IEEE Trans. Evol. Comput., 14, 942, 10.1109/TEVC.2010.2041061 · DOI
  120. Pillay, 2016, A review of hyper-heuristics for educational timetabling, Ann. Oper. Res., 239, 3, 10.1007/s10479-014-1688-1 · DOI
  121. Tsai, 2014, A hyper-heuristic scheduling algorithm for cloud, IEEE Trans. Cloud Comput., 2, 236, 10.1109/TCC.2014.2315797 · DOI
  122. Nguyen, 2015, Automatic programming via iterated local search for dynamic job shop scheduling, IEEE Trans. Cybern., 45, 1, 10.1109/TCYB.2014.2317488 · DOI
  123. Burke, 2006, Case-based heuristic selection for timetabling problems, J. Sched., 9, 115, 10.1007/s10951-006-6775-y · DOI
  124. Burke, 2007, A graph-based hyper-heuristic for educational timetabling problems, Eur. J. Oper. Res., 176, 177, 10.1016/j.ejor.2005.08.012 · DOI
  125. Burke, 2003, A tabu-search hyperheuristic for timetabling and rostering, J. Heuristics, 9, 451, 10.1023/B:HEUR.0000012446.94732.b6 · DOI
  126. Dowsland, 2007, A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation, Eur. J. Oper. Res., 179, 759, 10.1016/j.ejor.2005.03.058 · DOI
  127. Zheng, 2015, Emergency railway transportation planning using a hyper-heuristic approach, IEEE Trans. Intell. Transport. Syst., 16, 321, 10.1109/TITS.2014.2331239 · DOI
  128. Fukunaga, 2008, Automated discovery of local search heuristics for satisfiability testing, Evol. Comput., 16, 31, 10.1162/evco.2008.16.1.31 · DOI
  129. Ren, 2012, Hyper-heuristics with low level parameter adaptation, Evol. Comput., 20, 189, 10.1162/EVCO_a_00063 · DOI
  130. Geiger, 2006, Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach, J. Sched., 9, 7, 10.1007/s10951-006-5591-8 · DOI
  131. Bai, 2010, A hybrid evolutionary approach to the nurse rostering problem, IEEE Trans. Evol. Comput., 14, 580, 10.1109/TEVC.2009.2033583 · DOI
  132. Burke, 2012, Automating the packing heuristic design process with genetic programming, Evol. Comput., 20, 63, 10.1162/EVCO_a_00044 · DOI
  133. Sabar, 2013, Grammatical evolution hyper-heuristic for combinatorial optimization problems, IEEE Trans. Evol. Comput., 17, 840, 10.1109/TEVC.2013.2281527 · DOI
  134. Koohestani, 2014, Evolving an improved algorithm for envelope reduction using a hyper-heuristic approach, IEEE Trans. Evol. Comput., 18, 543, 10.1109/TEVC.2013.2281512 · DOI
  135. Huberman, 1997, An economics approach to hard computational problems, Science, 275, 51, 10.1126/science.275.5296.51 · DOI
  136. Skolicki, 2004, Improving evolutionary algorithms with multi-representation island models, vol. 3242, 420
  137. Lassig, 2014, General upper bounds on the runtime of parallel evolutionary algorithms, Evol. Comput., 22, 405, 10.1162/EVCO_a_00114 · DOI
  138. Nogueras, 2015, Studying fault-tolerance in island-based evolutionary and multimemetic algorithms, J. Grid Comput., 1
  139. Lässig, 2011, Analysis of speedups in parallel evolutionary algorithms for combinatorial optimization, vol. 7074, 405
  140. Araujo, 2011, Diversity through multiculturality: assessing migrant choice policies in an island model, IEEE Trans. Evol. Comput., 15, 456, 10.1109/TEVC.2010.2064322 · DOI
  141. Li, 2014, Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 18, 114, 10.1109/TEVC.2013.2239648 · DOI
  142. Fialho, 2008, Extreme value based adaptive operator selection, vol. 5199, 175
  143. Fialho, 2009, Dynamic multi-armed bandits and extreme value-based rewards for adaptive operator selection in evolutionary algorithms, vol. 5851, 176
  144. Thierens, 2007, Adaptive strategies for operator allocation, vol. 54, 77
  145. Auer, 2002, Finite-time analysis of the multiarmed bandit problem, Mach. Learn., 47, 235, 10.1023/A:1013689704352 · DOI · Paper page
  146. Fialho, 2010, Analyzing bandit-based adaptive operator selection mechanisms, Ann. Math. Artif. Intell., 60, 25, 10.1007/s10472-010-9213-y · DOI
  147. Wang, 2010, Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure, Soft Comput., 14, 193, 10.1007/s00500-008-0394-9 · DOI
  148. Li, 2011, Multi-objective differential evolution with adaptive control of parameters and operators, vol. 6683, 473
  149. Krempser, 2012, Adaptive operator selection at the hyper-level, vol. 7492, 378
  150. Suganthan, 1999, Particle swarm optimiser with neighbourhood operator, vol. 3, 1

Cited by

Sorted by citations

Metadata

Authors
Wu, G., Mallipeddi, R., & Suganthan, P. N.
Affiliations
  • Central South University (CN)
  • Kyungpook National University (KR)
  • Nanyang Technological University (SG)
Corresponding author
  • Mallipeddi, Rammohan
  • Suganthan, Ponnuthurai Nagaratnam
ORCID
Venue
Swarm and Evolutionary Computation
Publisher
Elsevier BV
Published
2019-02
Volume
44
Pages
695-711
ISSN
2210-6502 (ISSN-L)
DOI
10.1016/j.swevo.2018.08.015
OpenAlex
W2892369032
Cited by
270
References
2
Citations by year
2026: 14 · 2025: 38 · 2024: 29 · 2023: 51 · 2022: 36 · 2021: 40 · 2020: 40 · 2019: 20
FWCI
14.14
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Swarm and Evolutionary Computation (publishedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
Funders
  • National Research Foundation of Korea
  • Ministry of Education, Science and Technology (NRF- 2015R1C1A1A01055669)
  • Natural Science Foundation of Hunan Province (2017JJ3369)
  • National University of Defense Technology (ZK16-03-30)
  • National Natural Science Foundation of China (61603404)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
Keywords
ensemble methods, algorithm configuration, hyper-heuristics, island model, adaptive operator selection, constraint handling, niching, local search, surrogate models

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review