A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

[1]  Julian Togelius,et al.  Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization , 2020, IEEE Access.

[2]  Julian Togelius,et al.  ACM-DE: Adaptive p-best Cauchy Mutation with linear failure threshold reduction for Differential Evolution in numerical optimization , 2019, ArXiv.

[3]  Tsung-Che Chiang,et al.  Modified L-SHADE for Single Objective Real-Parameter Optimization , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

[4]  Hee Yong Youn,et al.  Adaptive Differential Evolution with Elite Opposition-Based Learning and its Application to Training Artificial Neural Networks , 2019, Fundam. Informaticae.

[5]  Tae Jong Choi,et al.  Accelerating differential evolution using multiple exponential cauchy mutation , 2018, GECCO.

[6]  Eugene Semenkin,et al.  LSHADE Algorithm with Rank-Based Selective Pressure Strategy for Solving CEC 2017 Benchmark Problems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[7]  Rawaa Dawoud Al-Dabbagh,et al.  Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy , 2018, Swarm Evol. Comput..

[8]  Shahryar Rahnamayan,et al.  Opposition based learning: A literature review , 2017, Swarm Evol. Comput..

[9]  Ponnuthurai N. Suganthan,et al.  Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction , 2017, Swarm Evol. Comput..

[10]  Haifeng Li,et al.  Ensemble of differential evolution variants , 2018, Inf. Sci..

[11]  Tae Jong Choi,et al.  Adaptive Cauchy Differential Evolution with Strategy Adaptation and Its Application to Training Large-Scale Artificial Neural Networks , 2017, BIC-TA.

[12]  Ponnuthurai N. Suganthan,et al.  Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[13]  Anas A. Hadi,et al.  LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[14]  Janez Brest,et al.  Single objective real-parameter optimization: Algorithm jSO , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[15]  Dalveer Kaur,et al.  Hybrid heuristic search method for design of digital IIR filter with conflicting objectives , 2017, Soft Comput..

[16]  Robert G. Reynolds,et al.  A novel differential crossover strategy based on covariance matrix learning with Euclidean neighborhood for solving real-world problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[17]  Tae Jong Choi,et al.  An Improved Differential Evolution Algorithm and Its Application to Large-Scale Artificial Neural Networks , 2017 .

[18]  Jian-Xin Xu,et al.  Multiple Exponential Recombination for Differential Evolution. , 2017, IEEE transactions on cybernetics.

[19]  Tae Jong Choi,et al.  Adaptive α-stable differential evolution in numerical optimization , 2017, Natural Computing.

[20]  Ju-Jang Lee,et al.  Stochastic Opposition-Based Learning Using a Beta Distribution in Differential Evolution , 2016, IEEE Transactions on Cybernetics.

[21]  Janez Brest,et al.  iL-SHADE: Improved L-SHADE algorithm for single objective real-parameter optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[22]  Robert G. Reynolds,et al.  An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[23]  Malabika Basu,et al.  Quasi-oppositional differential evolution for optimal reactive power dispatch , 2016 .

[24]  Ning Xiong,et al.  Adapting Differential Evolution Algorithms For Continuous Optimization Via Greedy Adjustment Of Control Parameters , 2016, J. Artif. Intell. Soft Comput. Res..

[25]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[26]  Guohua Wu,et al.  Differential evolution with multi-population based ensemble of mutation strategies , 2016, Inf. Sci..

[27]  Leandro dos Santos Coelho,et al.  An improved free search differential evolution algorithm: A case study on parameters identification of one diode equivalent circuit of a solar cell module , 2015 .

[28]  Tae Jong Choi,et al.  An Adaptive Population Resizing Scheme for Differential Evolution in Numerical Optimization , 2015 .

[29]  Amer Draa,et al.  A sinusoidal differential evolution algorithm for numerical optimisation , 2015, Appl. Soft Comput..

[30]  Ming Yang,et al.  Differential Evolution With Auto-Enhanced Population Diversity , 2015, IEEE Transactions on Cybernetics.

[31]  Tae Jong Choi,et al.  An Adaptive Cauchy Differential Evolution Algorithm with Population Size Reduction and Modified Multiple Mutation Strategies , 2015 .

[32]  Zhijian Wu,et al.  Improved differential evolution with adaptive opposition strategy , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[33]  Li Zhao,et al.  A review of opposition-based learning from 2005 to 2012 , 2014, Eng. Appl. Artif. Intell..

[34]  Millie Pant,et al.  Coordination of directional overcurrent relays using opposition based chaotic differential evolution algorithm , 2014 .

[35]  Tae Jong Choi,et al.  An Adaptive Cauchy Differential Evolution Algorithm with Bias Strategy Adaptation Mechanism for Global Numerical Optimization , 2014, J. Comput..

[36]  Nenad Mladenovic,et al.  DE-VNS: Self-adaptive Differential Evolution with crossover neighborhood search for continuous global optimization , 2013, Comput. Oper. Res..

[37]  Tae Jong Choi,et al.  An Adaptive Differential Evolution Algorithm with Automatic Population Resizing for Global Numerical Optimization , 2014, BIC-TA.

[38]  Wenyin Gong,et al.  Differential Evolution With Ranking-Based Mutation Operators , 2013, IEEE Transactions on Cybernetics.

[39]  Jinung An,et al.  An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization , 2013, TheScientificWorldJournal.

[40]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[41]  Silvia Curteanu,et al.  Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process , 2013, Appl. Soft Comput..

[42]  Zhijian Wu,et al.  Elite Opposition-Based Differential Evolution for Solving Large-Scale Optimization Problems and Its Implementation on GPU , 2012, 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies.

[43]  Ponnuthurai N. Suganthan,et al.  An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[44]  Adel Torkaman Rahmani,et al.  Molecular docking with opposition-based differential evolution , 2012, SAC '12.

[45]  Quanyuan Feng,et al.  A comparative study of crossover in differential evolution , 2011, J. Heuristics.

[46]  Zhijian Wu,et al.  Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems , 2011, Soft Comput..

[47]  Ponnuthurai N. Suganthan,et al.  Self-adaptive differential evolution with multi-trajectory search for large-scale optimization , 2011, Soft Comput..

[48]  Edoardo Amaldi,et al.  Ectropy of diversity measures for populations in Euclidean space , 2011, Inf. Sci..

[49]  Millie Pant,et al.  Improving the performance of differential evolution algorithm using Cauchy mutation , 2011, Soft Comput..

[50]  Shahryar Rahnamayan,et al.  Opposition-based Differential Evolution with protective generation jumping , 2011, 2011 IEEE Symposium on Differential Evolution (SDE).

[51]  Hui Li,et al.  Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[52]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[53]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[54]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[55]  Bidyadhar Subudhi,et al.  A differential evolution based neural network approach to nonlinear system identification , 2011, Appl. Soft Comput..

[56]  Shahryar Rahnamayan,et al.  Opposition based computing — A survey , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[57]  Ying Wang,et al.  Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling , 2010 .

[58]  Ville Tirronen,et al.  Recent advances in differential evolution: a survey and experimental analysis , 2010, Artificial Intelligence Review.

[59]  Dan Simon,et al.  Oppositional biogeography-based optimization , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[60]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[61]  Daniela Zaharie,et al.  Influence of crossover on the behavior of Differential Evolution Algorithms , 2009, Appl. Soft Comput..

[62]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[63]  Ponnuthurai N. Suganthan,et al.  Diversity enhanced Adaptive Evolutionary Programming for solving single objective constrained problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[64]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[65]  Francisco Herrera,et al.  Replacement strategies to preserve useful diversity in steady-state genetic algorithms , 2008, Inf. Sci..

[66]  Russell C. Eberhart,et al.  Population diversity of particle swarms , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[67]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[68]  Mario Ventresca,et al.  Oppositional Concepts in Computational Intelligence , 2008, Oppositional Concepts in Computational Intelligence.

[69]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[70]  Jing Jie,et al.  Particle Swarm Optimization with Diversity-Controlled Acceleration Coefficients , 2007, Third International Conference on Natural Computation (ICNC 2007).

[71]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution (ODE) with Variable Jumping Rate , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[72]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[73]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[74]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[75]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[76]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[77]  Dario Floreano,et al.  Measures of Diversity for Populations and Distances Between Individuals with Highly Reorganizable Genomes , 2004, Evolutionary Computation.

[78]  Mark Wineberg,et al.  The Underlying Similarity of Diversity Measures Used in Evolutionary Computation , 2003, GECCO.

[79]  Mark Wineberg,et al.  Distance between Populations , 2003, GECCO.

[80]  Rasmus K. Ursem,et al.  Diversity-Guided Evolutionary Algorithms , 2002, PPSN.

[81]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[82]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[83]  A. L. Barker,et al.  Dynamics of a distance-based population diversity measure , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[84]  A. L. Barker,et al.  Population Diversity and Fitness Measures Based on Genomic Distances , 1999 .

[85]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[86]  Edwin R. Hancock,et al.  Genetic algorithms for ambiguous labelling problems , 1997, Pattern Recognit..

[87]  Martin Zwick,et al.  Variance and Uncertainty Measures of Population Diversity Dynamics , 1995 .