Parameter Control in Evolutionary Algorithms: Trends and Challenges

More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state-of-the-art. We briefly summarize the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview, we observe trends in the area, identify some (methodological) shortcomings, and give recommendations for future research.

[1]  J. Davenport Editor , 1960 .

[2]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[5]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[6]  Editors , 1986, Brain Research Bulletin.

[7]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[9]  J. David Schaffer,et al.  An Adaptive Crossover Distribution Mechanism for Genetic Algorithms , 1987, ICGA.

[10]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[11]  In Schoenauer,et al.  Parallel Problem Solving from Nature , 1990, Lecture Notes in Computer Science.

[12]  Kenneth A. De Jong,et al.  An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms , 1990, PPSN.

[13]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[14]  Reinhard Männer,et al.  Towards an Optimal Mutation Probability for Genetic Algorithms , 1990, PPSN.

[15]  Bruce Tidor,et al.  Boltzmannn Weighted Selection Improves Performance of Genetic Algorithms , 1991 .

[16]  David B. Fogel,et al.  Meta-evolutionary programming , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[17]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[18]  Kalyanmoy Deb,et al.  Accounting for Noise in the Sizing of Populations , 1992, FOGA.

[19]  Thomas Bäck,et al.  The Interaction of Mutation Rate, Selection, and Self-Adaptation Within a Genetic Algorithm , 1992, PPSN.

[20]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[21]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[22]  Colin R. Reeves,et al.  Using Genetic Algorithms with Small Populations , 1993, ICGA.

[23]  Stephanie Forrest,et al.  Proceedings of the 5th International Conference on Genetic Algorithms , 1993 .

[24]  Atidel B. Hadj-Alouane,et al.  A dual genetic algorithm for bounded integer programs James C. Bean, Atidel Ben Hadj-Alouane. , 1993 .

[25]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[26]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[27]  Yukinori Kakazu,et al.  Controlling Dynamics of GA through Filtered Evaluation Function , 1994, PPSN.

[28]  Nikolaus Hansen,et al.  A Derandomized Approach to Self-Adaptation of Evolution Strategies , 1994, Evolutionary Computation.

[29]  Nikolaus Hansen,et al.  Step-Size Adaption Based on Non-Local Use of Selection Information , 1994, PPSN.

[30]  Zbigniew Michalewicz,et al.  Evolutionary optimization of constrained problems , 1994 .

[31]  Jan Paredis,et al.  Co-evolutionary Constraint Satisfaction , 1994, PPSN.

[32]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[33]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[34]  Peter J. Angeline,et al.  Adaptive and Self-adaptive Evolutionary Computations , 1995 .

[35]  William M. Spears,et al.  Adapting Crossover in Evolutionary Algorithms , 1995, Evolutionary Programming.

[36]  Harvey M. Wagner,et al.  Global Sensitivity Analysis , 1995, Oper. Res..

[37]  Günter Rudolph,et al.  A cellular genetic algorithm with self-adjusting acceptance threshold , 1995 .

[38]  Robert E. Smith,et al.  Adaptively Resizing Populations: Algorithm, Analysis, and First Results , 1993, Complex Syst..

[39]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[40]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[41]  A. E. Eiben,et al.  Self-adaptivity for constraint satisfaction: learning penalty functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[42]  Alice E. Smith,et al.  Penalty Functions , 1996 .

[43]  Thomas Bäck,et al.  Intelligent Mutation Rate Control in Canonical Genetic Algorithms , 1996, ISMIS.

[44]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[45]  Jim Smith,et al.  Self adaptation of mutation rates in a steady state genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[46]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[47]  Zbigniew Michalewicz,et al.  Self-Adaptive Genetic Algorithm for Numeric Functions , 1996, PPSN.

[48]  Jim Smith,et al.  Operator and parameter adaptation in genetic algorithms , 1997, Soft Comput..

[49]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

[50]  David E. Goldberg,et al.  The gambler''s ruin problem , 1997 .

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

[52]  Jano I. van Hemert,et al.  Graph Coloring with Adaptive Evolutionary Algorithms , 1998, J. Heuristics.

[53]  A. E. Eiben,et al.  On Evolutionary Exploration and Exploitation , 1998, Fundam. Informaticae.

[54]  Peter Ross,et al.  Adapting Operator Settings in Genetic Algorithms , 1998, Evolutionary Computation.

[55]  Marin Golub,et al.  Parallel Adaptive Genetic Algorithm , 1998, NC.

[56]  Vassilios Petridis,et al.  Varying Fitness Functions in Genetic Algorithms: Studying the Rate of Increase of the Dynamic Penalty Terms , 1998, PPSN.

[57]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[58]  Erick Cantú-Paz Migration Policies and Takeover Times in Genetic Algorithms , 1999, GECCO.

[59]  Erick Cantú-Paz,et al.  Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms , 1999, GECCO.

[60]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[61]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[62]  Tomoyuki Hiroyasu,et al.  Distributed genetic algorithms with randomized migration rate , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[63]  A. Rosa,et al.  An experimental study on dynamic random variation of population size , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[64]  Jano I. van Hemert,et al.  A Comparison of Genetic Programming Variants for Data Classification , 1999, IDA.

[65]  Ricard V. Solé,et al.  Evolutionary optimization through extinction dynamics , 1999 .

[66]  Jano van Hemert,et al.  SAW-ing EAs: adapting the fitness function for solving constrained problems , 1999 .

[67]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[68]  Thomas Bäck,et al.  An Empirical Study on GAs "Without Parameters" , 2000, PPSN.

[69]  René Thomsen,et al.  Applying Self-Organised Criticality to Evolutionary Algorithms , 2000, PPSN.

[70]  Agostinho C. Rosa,et al.  niGAVaPS — outbreeding in genetic algorithms , 2000, SAC '00.

[71]  Sana Ben Hamida,et al.  An Adaptive Algorithm for Constrained Optimization Problems , 2000, PPSN.

[72]  Günter Rudolph,et al.  Self-adaptive mutations may lead to premature convergence , 2001, IEEE Trans. Evol. Comput..

[73]  T. Krink,et al.  Self-organized criticality and mass extinction in evolutionary algorithms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[74]  Eric Kee,et al.  An adaptive genetic algorithm , 2001 .

[75]  Thomas D. LaToza,et al.  On the supply of building blocks , 2001 .

[76]  Jano I. van Hemert,et al.  Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems , 2001, EuroGP.

[77]  C. Fernandes,et al.  A study on non-random mating and varying population size in genetic algorithms using a royal road function , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[78]  Michael Affenzeller,et al.  Segregative Genetic Algorithms (SEGA): A hybrid superstructure upwards compatible to genetic algorithms for retarding premature convergence , 2001, Int. J. Comput. Syst. Signals.

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

[80]  Michael Affenzeller A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA) , 2001, IWANN.

[81]  A. E. Eiben,et al.  A critical note on experimental research methodology in EC , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[82]  Xiaodong Li,et al.  Parameter Control within a Co-operative Co-evolutionary Genetic Algorithm , 2002, PPSN.

[83]  Richard M. Everson,et al.  Controlling Genetic Algorithms With Reinforcement Learning , 2002, GECCO.

[84]  Xavier Bonnaire,et al.  Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms , 2002, ISMIS.

[85]  Thomas Jansen,et al.  An Analysis Of The Role Of Offspring Population Size In EAs , 2002, GECCO.

[86]  S.D. Muller,et al.  Step size adaptation in evolution strategies using reinforcement learning , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[87]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[88]  H. Abbass The self-adaptive Pareto differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[89]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[90]  Jonathan A. Wright,et al.  Self-adaptive fitness formulation for constrained optimization , 2003, IEEE Trans. Evol. Comput..

[91]  Rasmus K. Ursem,et al.  Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization , 2003 .

[92]  M. Tomassini,et al.  Saving computational effort in genetic programming by means of plagues , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[93]  David E. Goldberg,et al.  Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm , 2003, GECCO.

[94]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[95]  Kwong-Sak Leung,et al.  A novel approach in parameter adaptation and diversity maintenance for genetic algorithms , 2003, Soft Comput..

[96]  Francisco Fernández de Vega,et al.  Saving Resources with Plagues in Genetic Algorithms , 2004, PPSN.

[97]  Helio J. C. Barbosa,et al.  An adaptive penalty scheme for genetic algorithms in structural optimization , 2004 .

[98]  M. Narasimha Murty,et al.  Cauchy annealing schedule: an annealing schedule for Boltzmann selection scheme in evolutionary algorithms , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[99]  Edmund K. Burke,et al.  Parallel problem solving from nature - PPSN VIII : 8th International Conference, Birmingham, UK, September 18-22, 2004 : proceedings , 2004 .

[100]  Anne Auger,et al.  LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation , 2004, PPSN.

[101]  Elena Marchiori,et al.  Evolutionary Algorithms with On-the-Fly Population Size Adjustment , 2004, PPSN.

[102]  Keigo Watanabe,et al.  Evolutionary Optimization of Constrained Problems , 2004 .

[103]  Jonatan Gómez,et al.  Self Adaptation of Operator Rates in Evolutionary Algorithms , 2004, GECCO.

[104]  Nikolaus Hansen,et al.  Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.

[105]  Dirk Thierens,et al.  An Adaptive Pursuit Strategy for Allocating Operator Probabilities , 2005, BNAIC.

[106]  Yang Gao,et al.  SCGA: Controlling Genetic Algorithms with Sarsa(0) , 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).

[107]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[108]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[109]  David E. Goldberg,et al.  Online population size adjusting using noise and substructural measurements , 2005, 2005 IEEE Congress on Evolutionary Computation.

[110]  Heitor Silvério Lopes,et al.  Self-Adapting Evolutionary Parameters: Encoding Aspects for Combinatorial Optimization Problems , 2005, EvoCOP.

[111]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[112]  Neal Wagner,et al.  Genetic Programming with Efficient Population Control for Financial Time Series Prediction , 2005 .

[113]  Peter A. N. Bosman,et al.  Proceedings of the Genetic and Evolutionary Computation Conference - GECCO - 2006 , 2006 .

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

[115]  Rainer Laur,et al.  Parameter adaptation for differential evolution with design of experiments , 2006, Computational Intelligence.

[116]  Gary G. Yen,et al.  A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[117]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[118]  V. K. Koumousis,et al.  A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance , 2006, IEEE Transactions on Evolutionary Computation.

[119]  Edmund K. Burke,et al.  Parallel Problem Solving from Nature - PPSN IX: 9th International Conference, Reykjavik, Iceland, September 9-13, 2006, Proceedings , 2006, PPSN.

[120]  Jason Teo,et al.  Exploring dynamic self-adaptive populations in differential evolution , 2006, Soft Comput..

[121]  Agostinho C. Rosa,et al.  Self-regulated Population Size in Evolutionary Algorithms , 2006, PPSN.

[122]  Karl-Dirk Kammeyer,et al.  Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[123]  Martijn C. Schut,et al.  Is Self-adaptation of Selection Pressure and Population Size Possible? - A Case Study , 2006, PPSN.

[124]  Ruhul A. Sarker,et al.  Use of statistical outlier detection method in adaptive evolutionary algorithms , 2006, GECCO.

[125]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[126]  Cláudio F. Lima,et al.  Revisiting evolutionary algorithms with on-the-fly population size adjustment , 2006, GECCO '06.

[127]  Martijn C. Schut,et al.  Reinforcement Learning for Online Control of Evolutionary Algorithms , 2006, ESOA.

[128]  Yoichiro Maeda,et al.  Fuzzy adaptive search method for parallel genetic algorithm with island combination process , 2006, Int. J. Approx. Reason..

[129]  Mehmet Fatih Tasgetiren,et al.  Multi-objective optimization based on self-adaptive differential evolution algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[130]  Hans-Georg Beyer,et al.  Self-Adaptation in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[131]  Arthur C. Sanderson,et al.  JADE: Self-adaptive differential evolution with fast and reliable convergence performance , 2007, 2007 IEEE Congress on Evolutionary Computation.

[132]  David E. Goldberg,et al.  Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements , 2007, Parameter Setting in Evolutionary Algorithms.

[133]  Stefan Roth,et al.  Covariance Matrix Adaptation for Multi-objective Optimization , 2007, Evolutionary Computation.

[134]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[135]  Frédéric Saubion,et al.  Towards a generic control strategy for Evolutionary Algorithms: an adaptive fuzzy-learning approach , 2007, 2007 IEEE Congress on Evolutionary Computation.

[136]  Rainer Laur,et al.  Differential evolution with adaptive parameter setting for multi-objective optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[137]  Zbigniew Michalewicz,et al.  Parameter Adaptation for GP Forecasting Applications , 2007, Parameter Setting in Evolutionary Algorithms.

[138]  Cláudio F. Lima,et al.  Adaptive Population Sizing Schemes in Genetic Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[139]  Kenneth DeJong,et al.  Parameter Setting in EAs: a 30 Year Perspective , 2007, Parameter Setting in Evolutionary Algorithms.

[140]  Dirk Thierens,et al.  Adaptive Strategies for Operator Allocation , 2007, Parameter Setting in Evolutionary Algorithms.

[141]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[142]  Zbigniew Michalewicz,et al.  Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model , 2007, IEEE Transactions on Evolutionary Computation.

[143]  Frédéric Saubion,et al.  On the Design of Adaptive Control Strategies for Evolutionary Algorithms , 2007, Artificial Evolution.

[144]  Carlos Alberto Conceição António,et al.  Self-adaptation in Genetic Algorithms applied to structural optimization , 2008 .

[145]  Simon M. Lucas,et al.  Parallel Problem Solving from Nature - PPSN X, 10th International Conference Dortmund, Germany, September 13-17, 2008, Proceedings , 2008, PPSN.

[146]  Marco Ratto,et al.  Global Sensitivity Analysis , 2008 .

[147]  Oliver Kramer Self-Adaptive Heuristics for Evolutionary Computation , 2008, Studies in Computational Intelligence.

[148]  Rainer Laur,et al.  Comparison of Adaptive Approaches for Differential Evolution , 2008, PPSN.

[149]  Bernhard Sendhoff,et al.  Covariance Matrix Adaptation Revisited - The CMSA Evolution Strategy - , 2008, PPSN.

[150]  Ali Kaveh,et al.  Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization , 2008 .

[151]  Frédéric Saubion,et al.  A Compass to Guide Genetic Algorithms , 2008, PPSN.

[152]  Weiyi Qian,et al.  Adaptive differential evolution algorithm for multiobjective optimization problems , 2008, Appl. Math. Comput..

[153]  María Cristina Riff,et al.  Self-calibrating Strategies for Evolutionary Approaches that Solve Constrained Combinatorial Problems , 2008, ISMIS.

[154]  Michèle Sebag,et al.  Adaptive operator selection with dynamic multi-armed bandits , 2008, GECCO '08.

[155]  Janez Brest,et al.  An Analysis of the Control Parameters’ Adaptation in DE , 2008 .

[156]  Dirk V. Arnold,et al.  Step Length Adaptation on Ridge Functions , 2008, Evolutionary Computation.

[157]  J. Clune,et al.  Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes , 2008, PLoS computational biology.

[158]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[159]  Agostinho C. Rosa,et al.  A self-organized criticality mutation operator for dynamic optimization problems , 2008, GECCO '08.

[160]  Mauro Brunato,et al.  Reactive Search and Intelligent Optimization , 2008 .

[161]  Michèle Sebag,et al.  Extreme Value Based Adaptive Operator Selection , 2008, PPSN.

[162]  Jorge Maturana,et al.  Contrôle Générique de Paramètres pour les Algorithmes Evolutionnaires. (Generic Parameter Control for Evolutionary Algorithms) , 2009 .

[163]  Zhun Fan,et al.  Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique , 2009 .

[164]  Ponnuthurai N. Suganthan,et al.  Multi-objective optimization using self-adaptive differential evolution algorithm , 2009, 2009 IEEE Congress on Evolutionary Computation.

[165]  Nikolaus Hansen,et al.  Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed , 2009, GECCO '09.

[166]  Christian Gagné,et al.  Improving genetic algorithms performance via deterministic population shrinkage , 2009, GECCO.

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

[168]  Yihong Ru,et al.  Improved Adaptive and Multi-group Parallel Genetic Algorithm Based on Good-point Set , 2009, J. Softw..

[169]  Michèle Sebag,et al.  Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection , 2009, 2009 IEEE Congress on Evolutionary Computation.

[170]  P. P. Chakrabarti,et al.  Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off , 2009, Appl. Soft Comput..

[171]  Nikolaus Hansen,et al.  Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed , 2009, GECCO '09.

[172]  Swagatam Das,et al.  A fitness-based adaptation scheme for control parameters in differential evolution , 2010, GECCO '10.

[173]  Ting Hu,et al.  Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm , 2010, Genetic Programming and Evolvable Machines.

[174]  Michèle Sebag,et al.  Analyzing bandit-based adaptive operator selection mechanisms , 2010, Annals of Mathematics and Artificial Intelligence.

[175]  Álvaro Fialho,et al.  Adaptive strategy selection in differential evolution , 2010, GECCO '10.

[176]  Benjamin Doerr,et al.  Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem , 2010, PPSN.

[177]  Raymond Ros,et al.  Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed , 2010, GECCO '10.

[178]  Peter C. Nelson,et al.  An explorative and exploitative mutation scheme , 2010, IEEE Congress on Evolutionary Computation.

[179]  Adrien Goëffon,et al.  A Dynamic Island-Based Genetic Algorithms Framework , 2010, SEAL.

[180]  Yoshitaka Sakurai,et al.  A Method to Control Parameters of Evolutionary Algorithms by Using Reinforcement Learning , 2010, 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems.

[181]  Daniel R. Tauritz,et al.  An exploration into dynamic population sizing , 2010, GECCO '10.

[182]  Oliver Kramer,et al.  Evolutionary self-adaptation: a survey of operators and strategy parameters , 2010, Evol. Intell..

[183]  Silvestre Fialho,et al.  Adaptive operator selection for optimization , 2010 .

[184]  Frédéric Saubion,et al.  Autonomous operator management for evolutionary algorithms , 2010, J. Heuristics.

[185]  María Cristina Riff,et al.  On-the-fly calibrating strategies for evolutionary algorithms , 2011, Inf. Sci..

[186]  Thomas Stützle,et al.  Off-line and On-line Tuning: A Study on Operator Selection for a Memetic Algorithm Applied to the QAP , 2011, EvoCOP.

[187]  A. E. Eiben,et al.  Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..

[188]  Álvaro Fialho,et al.  Multi-Objective Differential Evolution with Adaptive Control of Parameters and Operators , 2011, LION.

[189]  Irene Moser,et al.  Predictive parameter control , 2011, GECCO '11.

[190]  Ahamad Tajudin Abdul Khader,et al.  A parameter-less genetic algorithm with customized crossover and mutation operators , 2011, GECCO '11.

[191]  Xavier Blasco Ferragud,et al.  An empirical study on parameter selection for multiobjective optimization algorithms using Differential Evolution , 2011, 2011 IEEE Symposium on Differential Evolution (SDE).

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

[193]  Fearghal Morgan,et al.  Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection , 2011, IEEE Transactions on Evolutionary Computation.

[194]  Swagatam Das,et al.  An improved differential evolution algorithm with fitness-based adaptation of the control parameters , 2011, Inf. Sci..

[195]  Agostinho C. Rosa,et al.  The Sandpile Mutation Operator for Genetic Algorithms , 2011, LION.

[196]  Arina Buzdalova,et al.  Choosing Best Fitness Function with Reinforcement Learning , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[197]  Agostinho C. Rosa,et al.  A Study on the Mutation Rates of a Genetic Algorithm Interacting with a Sandpile , 2011, EvoApplications.

[198]  Fernando G. Lobo Idealized dynamic population sizing for uniformly scaled problems , 2011, GECCO '11.

[199]  Alex S. Fukunaga,et al.  Distributed island-model genetic algorithms using heterogeneous parameter settings , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[200]  Jun Zhang,et al.  Co-evolutionary differential evolution with dynamic population size and adaptive migration strategy , 2011, GECCO '11.

[201]  A. E. Eiben,et al.  Exploratory analysis of an on-line evolutionary algorithm in simulated robots , 2012, Evol. Intell..

[202]  Sanaz Mostaghim,et al.  Adaptive Range Parameter Control , 2012, 2012 IEEE Congress on Evolutionary Computation.

[203]  Eric Monfroy,et al.  Autonomous Search , 2012, Springer Berlin Heidelberg.

[204]  W. Marsden I and J , 2012 .

[205]  S. Smit Parameter Tuning and Scientific Testing in Evolutionary Algorithms , 2012 .

[206]  Frédéric Saubion,et al.  A Comparison of Operator Utility Measures for On-Line Operator Selection in Local Search , 2012, LION.

[207]  Zhaolu Guo,et al.  Self-adaptive Differential Evolution Based Multi-objective Optimization Incorporating Local Search and Indicator-Based Selection , 2012, ICIC.

[208]  Arina Buzdalova,et al.  Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning , 2012, 2012 11th International Conference on Machine Learning and Applications.

[209]  Günter Rudolph,et al.  Evolutionary Strategies , 2012, Handbook of Natural Computing.

[210]  A. E. Eiben,et al.  A Generic Approach to Parameter Control , 2012, EvoApplications.

[211]  Aldeida Aleti,et al.  An adaptive approach to controlling parameters of evolutionary algorithms , 2012 .

[212]  A. E. Eiben,et al.  Self-adapting fitness evaluation times for on-line evolution of simulated robots , 2013, GECCO '13.

[213]  Marco Montemurro,et al.  The Automatic Dynamic Penalisation method (ADP) for handling constraints with genetic algorithms , 2013 .

[214]  David Millán-Ruiz,et al.  Matching island topologies to problem structure in parallel evolutionary algorithms , 2013, Soft Computing.

[215]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[216]  Mark Hoogendoorn,et al.  Why parameter control mechanisms should be benchmarked against random variation , 2013, 2013 IEEE Congress on Evolutionary Computation.

[217]  Qingfu Zhang,et al.  Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition , 2014, IEEE Transactions on Evolutionary Computation.

[218]  Thomas Stützle,et al.  Proceedings of the Genetic and Evolutionary Computation Conference , 2017, GECCO.