A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

The theory of evolutionary computation for discrete search spaces has made a lot of progress during the last ten years. This survey summarizes some of the most important recent results obtained in this research area. It reviews important methods such as drift analysis, discusses theoretical insight on parameter tuning and parameter control, and summarizes the advances made for stochastic and dynamic problems. Furthermore, the survey highlights important results in the area of combinatorial optimization with a focus on parameterized complexity and the optimization of submodular functions. Finally, it gives an overview on the large amount of new important results for estimation of distribution algorithms.

[1]  Stephen R. Marsland,et al.  Convergence Properties of (μ + λ) Evolutionary Algorithms , 2011, AAAI.

[2]  Andrew M. Sutton,et al.  On the runtime dynamics of the compact genetic algorithm on jump functions , 2018, GECCO.

[3]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[4]  Benjamin Doerr,et al.  Run-time analysis of the (1+1) evolutionary algorithm optimizing linear functions over a finite alphabet , 2012, GECCO '12.

[5]  Stefan Droste,et al.  Analysis of the (1+1) EA for a dynamically changing ONEMAX-variant , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Fernando G. Lobo,et al.  Lost gems of EC: The Equilibrium Genetic Algorithm and the Role of Crossover , 2007, SEVO.

[7]  Adam Prügel-Bennett,et al.  When a genetic algorithm outperforms hill-climbing , 2004, Theor. Comput. Sci..

[8]  Benjamin Doerr,et al.  Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift , 2020, Evolutionary Computation.

[9]  Benjamin Doerr,et al.  Optimal Static and Self-Adjusting Parameter Choices for the (1+(λ,λ))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$( , 2017, Algorithmica.

[10]  Dirk Sudholt,et al.  Runtime analysis of randomized search heuristics for dynamic graph coloring , 2019, GECCO.

[11]  Per Kristian Lehre,et al.  Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation , 2010, PPSN.

[12]  Benjamin Doerr,et al.  Significance-Based Estimation-of-Distribution Algorithms , 2018, IEEE Transactions on Evolutionary Computation.

[13]  B. Hajek.  Hitting-time and occupation-time bounds implied by drift analysis with applications , 1982, Advances in Applied Probability.

[14]  Andrew M. Sutton,et al.  Optimization of Chance-Constrained Submodular Functions , 2019, AAAI.

[15]  Timo Kötzing,et al.  ACO Beats EA on a Dynamic Pseudo-Boolean Function , 2012, PPSN.

[16]  Madeleine Theile,et al.  Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm , 2009, EvoCOP.

[17]  Adrian Kosowski,et al.  Population Protocols Are Fast , 2018 .

[18]  Benjamin Doerr,et al.  Static and Self-Adjusting Mutation Strengths for Multi-valued Decision Variables , 2018, Algorithmica.

[19]  Jonathan L. Shapiro,et al.  Diversity Loss in General Estimation of Distribution Algorithms , 2006, PPSN.

[20]  Frank Neumann,et al.  Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem , 2020, GECCO.

[21]  Carsten Witt,et al.  (1+1) EA on Generalized Dynamic OneMax , 2015, FOGA.

[22]  Frank Neumann,et al.  Maximizing Submodular Functions under Matroid Constraints by Multi-objective Evolutionary Algorithms , 2014, PPSN.

[23]  Per Kristian Lehre,et al.  Fitness-levels for non-elitist populations , 2011, GECCO '11.

[24]  Carsten Witt,et al.  Runtime Analysis of the ( μ +1) EA on Simple Pseudo-Boolean Functions , 2006 .

[25]  Tobias Storch,et al.  Finding large cliques in sparse semi-random graphs by simple randomized search heuristics , 2007, Theor. Comput. Sci..

[26]  Frank Neumann,et al.  Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms , 2020, PPSN.

[27]  Vahid Roostapour,et al.  Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem , 2020, ArXiv.

[28]  Alistair Sinclair,et al.  The Equilibrium Genetic Algorithm and the Role of Crossover , 1993 .

[29]  Pietro Simone Oliveto,et al.  Theoretical analysis of fitness-proportional selection: landscapes and efficiency , 2009, GECCO.

[30]  Benjamin Doerr,et al.  Non-existence of linear universal drift functions , 2010, Theor. Comput. Sci..

[31]  Emo Welzl,et al.  An Optimal Decentralized $(Δ+ 1)$-Coloring Algorithm , 2020, ESA.

[32]  Mikkel T. Jensen,et al.  Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation , 2004, J. Math. Model. Algorithms.

[33]  Timo Kötzing,et al.  Optimizing expected path lengths with ant colony optimization using fitness proportional update , 2013, FOGA XII '13.

[34]  Michael R. Fellows,et al.  Parameterized Complexity , 1998 .

[35]  Benjamin Doerr,et al.  From black-box complexity to designing new genetic algorithms , 2015, Theor. Comput. Sci..

[36]  Duc-Cuong Dang,et al.  Simplified Runtime Analysis of Estimation of Distribution Algorithms , 2015, GECCO.

[37]  Jens Jägersküpper,et al.  A Blend of Markov-Chain and Drift Analysis , 2008, PPSN.

[38]  Benjamin Doerr,et al.  The (1+λ) evolutionary algorithm with self-adjusting mutation rate , 2017, GECCO.

[39]  Kenneth A. De Jong,et al.  Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods on the Choice of the Offspring Population Size in Evolutionary Algorithms on the Choice of the Offspring Population Size in Evolutionary Algorithms , 2004 .

[40]  Angelika Steger,et al.  When Does Hillclimbing Fail on Monotone Functions: An entropy compression argument , 2018, ANALCO.

[41]  Kalyanmoy Deb,et al.  Evolutionary algorithms in large-scale open pit mine scheduling , 2010, GECCO '10.

[42]  Petr Posík.  Estimation of Distribution Algorithms , 2006 .

[43]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[44]  Ingo Wegener,et al.  Theoretical Aspects of Evolutionary Algorithms , 2001, ICALP.

[45]  Mojgan Pourhassan,et al.  Parameterized Analysis of Multiobjective Evolutionary Algorithms and the Weighted Vertex Cover Problem , 2019, Evolutionary Computation.

[46]  Benjamin Doerr.  Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift , 2020, Evolutionary Computation.

[47]  Andreas Krause,et al.  Submodular Function Maximization , 2014, Tractability.

[48]  Benjamin Doerr,et al.  Multiplicative Drift Analysis , 2010, GECCO '10.

[49]  Carsten Witt,et al.  Optimal Mutation Rates for the (1+λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}) EA on One , 2017, Algorithmica.

[50]  Benjamin Doerr,et al.  Analyzing randomized search heuristics via stochastic domination , 2019, Theor. Comput. Sci..

[51]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[52]  Thomas Jansen,et al.  Mutation Rate Matters Even When Optimizing Monotonic Functions , 2013, Evolutionary Computation.

[53]  Benjamin Doerr,et al.  First Steps Towards a Runtime Analysis When Starting With a Good Solution , 2020, PPSN.

[54]  Carsten Witt,et al.  Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models , 2007, Evolutionary Computation.

[55]  Per Kristian Lehre,et al.  On the limitations of the univariate marginal distribution algorithm to deception and where bivariate EDAs might help , 2019, FOGA '19.

[56]  Pietro Simone Oliveto,et al.  Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation , 2008, Algorithmica.

[57]  Carsten Witt,et al.  Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax , 2017, FOGA '17.

[58]  Chao Qian,et al.  Running time analysis of the (1+1)-EA for onemax and leadingones under bit-wise noise , 2017, GECCO.

[59]  Thomas Jansen,et al.  Dynamic Parameter Control in Simple Evolutionary Algorithms , 2001, FOGA.

[60]  Dirk Sudholt,et al.  Medium step sizes are harmful for the compact genetic algorithm , 2018, GECCO.

[61]  Andrew M. Sutton,et al.  Fixed-parameter evolutionary algorithms for the Euclidean Traveling Salesperson problem , 2013, 2013 IEEE Congress on Evolutionary Computation.

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

[63]  Benjamin Doerr,et al.  Fast genetic algorithms , 2017, GECCO.

[64]  Thomas Jansen,et al.  Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods On the Choice of the Mutation Probability for the ( 1 + 1 ) EA , 2006 .

[65]  Mojgan Pourhassan,et al.  Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint , 2019, Algorithmica.

[66]  Markus H ohfeld,et al.  Random keys genetic algorithm with adaptive penalty function for optimization of constrained facility layout problems , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[67]  Frank Neumann,et al.  Parameterized complexity analysis and more effective construction methods for ACO algorithms and the euclidean traveling salesperson problem , 2013, 2013 IEEE Congress on Evolutionary Computation.

[68]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[69]  Benjamin Doerr,et al.  The efficiency threshold for the offspring population size of the (µ, λ) EA , 2019, GECCO.

[70]  Markus Wagner,et al.  Evolutionary algorithms and submodular functions: benefits of heavy-tailed mutations , 2018, Natural Computing.

[71]  Jonathan E. Rowe,et al.  Linear multi-objective drift analysis , 2018, Theor. Comput. Sci..

[72]  Dirk Sudholt,et al.  A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms , 2011, IEEE Transactions on Evolutionary Computation.

[73]  Schloss Birlinghoven,et al.  How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .

[74]  Dirk Sudholt,et al.  The choice of the offspring population size in the (1, λ) evolutionary algorithm , 2014, Theor. Comput. Sci..

[75]  Markus Wagner,et al.  A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters , 2020, Inf. Sci..

[76]  Mojgan Pourhassan,et al.  Maintaining 2-Approximations for the Dynamic Vertex Cover Problem Using Evolutionary Algorithms , 2015, GECCO.

[77]  Benjamin Doerr,et al.  Fixed-Target Runtime Analysis , 2020, Algorithmica.

[78]  Frank Neumann,et al.  Fixed-Parameter Evolutionary Algorithms and the Vertex Cover Problem , 2012, Algorithmica.

[79]  Holger R. Maier,et al.  Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge , 2015, Environ. Model. Softw..

[80]  Jonathan E. Rowe,et al.  Theoretical analysis of local search strategies to optimize network communication subject to preserving the total number of links , 2009, Int. J. Intell. Comput. Cybern..

[81]  Benjamin Doerr,et al.  Optimal Parameter Choices via Precise Black-Box Analysis , 2016, GECCO.

[82]  Thomas Bck,et al.  Self-adaptation in genetic algorithms , 1991 .

[83]  Carsten Witt,et al.  Runtime Analysis of the ( + 1) EA on Simple Pseudo-Boolean Functions , 2006, Evolutionary Computation.

[84]  Carsten Witt,et al.  Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions† , 2013, Combinatorics, Probability and Computing.

[85]  Duc-Cuong Dang,et al.  Level-Based Analysis of Genetic Algorithms and Other Search Processes , 2014, bioRxiv.

[86]  Markus Wagner,et al.  Fast and effective multi-objective optimisation of wind turbine placement , 2013, GECCO '13.

[87]  Martin Pelikan,et al.  An introduction and survey of estimation of distribution algorithms , 2011, Swarm Evol. Comput..

[88]  Marvin Künnemann,et al.  Optimizing linear functions with the (1+λ) evolutionary algorithm - Different asymptotic runtimes for different instances , 2015, Theor. Comput. Sci..

[89]  Gerhard J. Woeginger,et al.  The Traveling Salesman Problem with Few Inner Points , 2004, COCOON.

[90]  Daniel Johannsen,et al.  Random combinatorial structures and randomized search heuristics , 2010 .

[91]  Marc Schoenauer,et al.  Rigorous Hitting Times for Binary Mutations , 1999, Evolutionary Computation.

[92]  Frank Neumann,et al.  Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem , 2010, PPSN.

[93]  Frank Neumann,et al.  Bioinspired computation in combinatorial optimization: algorithms and their computational complexity , 2010, GECCO '12.

[94]  Benjamin Doerr,et al.  Bivariate estimation-of-distribution algorithms can find an exponential number of optima , 2020, GECCO.

[95]  Martin Dostál,et al.  Evolutionary Music Composition , 2013, Handbook of Optimization.

[96]  Anne Auger,et al.  Drift theory in continuous search spaces: expected hitting time of the (1 + 1)-ES with 1/5 success rule , 2018, GECCO.

[97]  Jonathan L. Shapiro,et al.  The Sensitivity of PBIL to Its Learning Rate, and How Detailed Balance Can Remove It , 2002, FOGA.

[98]  Vahab S. Mirrokni,et al.  Non-monotone submodular maximization under matroid and knapsack constraints , 2009, STOC '09.

[99]  Frank Neumann,et al.  A Parameterized Runtime Analysis of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem , 2012, AAAI.

[100]  Per Kristian Lehre,et al.  When is an estimation of distribution algorithm better than an evolutionary algorithm? , 2009, 2009 IEEE Congress on Evolutionary Computation.

[101]  Stefan Droste,et al.  A rigorous analysis of the compact genetic algorithm for linear functions , 2006, Natural Computing.

[102]  Per Kristian Lehre,et al.  Drift analysis , 2012, GECCO '12.

[103]  Dirk Sudholt,et al.  On the robustness of evolutionary algorithms to noise: refined results and an example where noise helps , 2018, GECCO.

[104]  Carsten Witt,et al.  The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic Optimization , 2017, Algorithmica.

[105]  Frank Neumann,et al.  Maximizing Submodular or Monotone Functions under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms , 2020, PPSN.

[106]  Ingo Wegener,et al.  The analysis of evolutionary algorithms on sorting and shortest paths problems , 2004, J. Math. Model. Algorithms.

[107]  Per Kristian Lehre,et al.  Runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise , 2019, GECCO.

[108]  Carsten Witt,et al.  The Interplay of Population Size and Mutation Probability in the (1+λ) EA on OneMax , 2017, Algorithmica.

[109]  Abhimanyu Das,et al.  Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection , 2011, ICML.

[110]  Markus Wagner,et al.  Escaping large deceptive basins of attraction with heavy-tailed mutation operators , 2018, GECCO.

[111]  Dirk Sudholt,et al.  On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization , 2018, Algorithmica.

[112]  Zbigniew Michalewicz,et al.  Evolutionary Computation for Real-World Problems , 2016, Challenges in Computational Statistics and Data Mining.

[113]  Benjamin Doerr,et al.  Drift analysis , 2011, GECCO.

[114]  Thomas Jansen,et al.  Approximating vertex cover using edge-based representations , 2013, FOGA XII '13.

[115]  Benjamin Doerr,et al.  The Runtime of the Compact Genetic Algorithm on Jump Functions , 2019, Algorithmica.

[116]  Frank Neumann,et al.  Evolutionary algorithms for the chance-constrained knapsack problem , 2019, GECCO.

[117]  Benjamin Doerr,et al.  Runtime Analysis of a Heavy-Tailed (1+(λ, λ)) Genetic Algorithm on Jump Functions , 2020, PPSN.

[118]  Dirk Sudholt,et al.  A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems , 2012, Algorithmica.

[119]  Benjamin Doerr,et al.  A tight runtime analysis for the cGA on jump functions: EDAs can cross fitness valleys at no extra cost , 2019, GECCO.

[120]  Pietro Simone Oliveto,et al.  On the runtime analysis of selection hyper-heuristics with adaptive learning periods , 2018, GECCO.

[121]  Robert Elsässer,et al.  The impact of the power law exponent on the behavior of a dynamic epidemic type process , 2012, SPAA '12.

[122]  Mark Hoogendoorn,et al.  Parameter Control in Evolutionary Algorithms: Trends and Challenges , 2015, IEEE Transactions on Evolutionary Computation.

[123]  Leslie Ann Goldberg,et al.  Adaptive Drift Analysis , 2011, Algorithmica.

[124]  Benjamin Doerr,et al.  Multiplicative Up-Drift , 2019, Algorithmica.

[125]  Andrew M. Sutton,et al.  Resampling vs Recombination: a Statistical Run Time Estimation , 2017, FOGA '17.

[126]  Benjamin Doerr,et al.  Runtime analysis of the (1+1) evolutionary algorithm on strings over finite alphabets , 2011, FOGA '11.

[127]  Xin Yao,et al.  A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[128]  Mojgan Pourhassan,et al.  Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments , 2018, Theory of Evolutionary Computation.

[129]  Thomas Jansen,et al.  On the brittleness of evolutionary algorithms , 2007, FOGA'07.

[130]  Frank Neumann,et al.  Time Complexity Analysis of Evolutionary Algorithms on Random Satisfiable k-CNF Formulas , 2016, Algorithmica.

[131]  Frank Neumann,et al.  On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling , 2015, IJCAI.

[132]  Benjamin Doerr,et al.  Edge-based representation beats vertex-based representation in shortest path problems , 2010, GECCO '10.

[133]  Frank Neumann,et al.  Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives , 2020, ECAI.

[134]  Yang Yu,et al.  An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints , 2020, AAAI.

[135]  Andrew M. Sutton,et al.  Runtime analysis of the (1 + 1) evolutionary algorithm for the chance-constrained knapsack problem , 2019, FOGA '19.

[136]  Dirk Sudholt,et al.  Adaptive population models for offspring populations and parallel evolutionary algorithms , 2011, FOGA '11.

[137]  Yang Yu,et al.  Subset Selection by Pareto Optimization , 2015, NIPS.

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

[139]  Per Kristian Lehre,et al.  Negative Drift in Populations , 2010, PPSN.

[140]  Yang Yu,et al.  On Subset Selection with General Cost Constraints , 2017, IJCAI.

[141]  Benjamin Doerr,et al.  Ants easily solve stochastic shortest path problems , 2012, GECCO '12.

[142]  Xin Yao,et al.  Analysis of Computational Time of Simple Estimation of Distribution Algorithms , 2010, IEEE Transactions on Evolutionary Computation.

[143]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[144]  Matthew R. Lewis.  Evolutionary Visual Art and Design , 2008, The Art of Artificial Evolution.

[145]  Pietro Simone Oliveto,et al.  Erratum: Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation , 2008, PPSN.

[146]  Benjamin Doerr,et al.  k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation , 2016, PPSN.

[147]  Frank Neumann,et al.  Randomized Local Search, Evolutionary Algorithms, and the Minimum Spanning Tree Problem , 2004, GECCO.

[148]  Dirk Sudholt,et al.  Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms , 2015, Evolutionary Computation.

[149]  Jonathan L. Shapiro,et al.  Drift and Scaling in Estimation of Distribution Algorithms , 2005, Evolutionary Computation.

[150]  Xin Yao,et al.  Drift analysis and average time complexity of evolutionary algorithms , 2001, Artif. Intell..

[151]  Tobias Friedrich,et al.  EDAs cannot be Balanced and Stable , 2016, GECCO.

[152]  Tobias Storch,et al.  How randomized search heuristics find maximum cliques in planar graphs , 2006, GECCO.

[153]  Carsten Witt,et al.  Optimal Mutation Rates for the (1+$$\lambda $$λ) EA on OneMax Through Asymptotically Tight Drift Analysis , 2017, Algorithmica.

[154]  Carsten Witt,et al.  The Interplay of Population Size and Mutation Probability in the ($$1+\lambda $$1+λ) EA on OneMax , 2016, Algorithmica.

[155]  Chao Qian,et al.  Running Time Analysis of the (1+1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1+1$$\end{document})-EA for OneMax an , 2017, Algorithmica.

[156]  Chao Qian,et al.  Dynamic Mutation Based Pareto Optimization for Subset Selection , 2018, ICIC.

[157]  Carsten Witt,et al.  Upper Bounds on the Running Time of the Univariate Marginal Distribution Algorithm on OneMax , 2018, Algorithmica.

[158]  Benjamin Doerr.  Does comma selection help to cope with local optima? , 2020, GECCO.

[159]  Pietro Simone Oliveto,et al.  Improved time complexity analysis of the Simple Genetic Algorithm , 2015, Theor. Comput. Sci..

[160]  Mahmoud Fouz,et al.  Sharp bounds by probability-generating functions and variable drift , 2011, GECCO '11.

[161]  Weijie Zheng,et al.  Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms , 2020, IEEE Transactions on Evolutionary Computation.

[162]  Zbigniew Michalewicz,et al.  Multi-mine Planning using a Multi-objective Evolutionary Algorithm , 2013, 2013 IEEE Congress on Evolutionary Computation.

[163]  Chao Qian,et al.  Towards a Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes Under General Bit-Wise Noise , 2018, PPSN.

[164]  Angelika Steger,et al.  Drift Analysis and Evolutionary Algorithms Revisited , 2016, Combinatorics, Probability and Computing.

[165]  Per Kristian Lehre,et al.  Improved runtime bounds for the univariate marginal distribution algorithm via anti-concentration , 2017, GECCO.

[166]  Frank Neumann,et al.  Parameterized Runtime Analyses of Evolutionary Algorithms for the Planar Euclidean Traveling Salesperson Problem , 2014, Evolutionary Computation.

[167]  Frank Neumann,et al.  On the Effects of Adding Objectives to Plateau Functions , 2009, IEEE Transactions on Evolutionary Computation.

[168]  Frank Neumann,et al.  Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints , 2018, AAAI.

[169]  Pietro Simone Oliveto,et al.  Evolving boolean functions with conjunctions and disjunctions via genetic programming , 2019, GECCO.

[170]  Andrew M. Sutton,et al.  The Compact Genetic Algorithm is Efficient Under Extreme Gaussian Noise , 2017, IEEE Transactions on Evolutionary Computation.

[171]  Per Kristian Lehre,et al.  Unbiased Black-Box Complexity of Parallel Search , 2014, PPSN.

[172]  Christopher R. Stephens,et al.  "Optimal" mutation rates for genetic search , 2006, GECCO.

[173]  Dorian Nogneng,et al.  A new analysis method for evolutionary optimization of dynamic and noisy objective functions , 2018, GECCO.

[174]  Carsten Witt,et al.  MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions , 2014, Algorithmica.

[175]  Duc-Cuong Dang,et al.  Self-adaptation of Mutation Rates in Non-elitist Populations , 2016, PPSN.

[176]  Stefan Droste,et al.  Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Analysis of the (1+1) EA for a Noisy OneMax , 2004 .

[177]  Martin S. Krejca,et al.  Intuitive Analyses via Drift Theory , 2018 .

[178]  Gabriela Ochoa,et al.  Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic , 2002, GECCO.

[179]  Jens Jägersküpper,et al.  When the Plus Strategy Outperforms the Comma Strategyand When Not , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[180]  Benjamin Doerr,et al.  The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis , 2020, Evolutionary Computation.

[181]  Mojgan Pourhassan,et al.  Runtime Analysis of RLS and (1+1) EA for the Dynamic Weighted Vertex Cover Problem , 2019, Theor. Comput. Sci..

[182]  Benjamin Doerr,et al.  Runtime analysis of the (1 + (λ, λ)) genetic algorithm on random satisfiable 3-CNF formulas , 2017, GECCO.

[183]  Carsten Witt,et al.  Theory of estimation-of-distribution algorithms , 2018, GECCO.

[184]  Frank Neumann,et al.  Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints , 2018, Algorithmica.

[185]  Dirk Sudholt,et al.  More effective randomized search heuristics for graph coloring through dynamic optimization , 2020, GECCO.

[186]  Thomas Jansen,et al.  On the analysis of the (1+1) evolutionary algorithm , 2002, Theor. Comput. Sci..

[187]  Carsten Witt,et al.  Self-Adjusting Evolutionary Algorithms for Multimodal Optimization , 2020, Algorithmica.

[188]  Carsten Witt,et al.  Runtime analysis of ant colony optimization on dynamic shortest path problems , 2013, GECCO '13.

[189]  Zhi-Hua Zhou,et al.  Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms , 2017, Artif. Intell..

[190]  Stefan Droste,et al.  Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Analysis of the (1+1) Ea for a Dynamically Bitwise Changing Onemax Analysis of the (1+1) Ea for a Dynamically Bitwise Changing Onemax , 2003 .

[191]  Markus Wagner,et al.  Heavy-Tailed Mutation Operators in Single-Objective Combinatorial Optimization , 2018, PPSN.

[192]  Frank Neumann,et al.  Evolutionary Image Transition and Painting Using Random Walks , 2020, Evolutionary Computation.

[193]  Frank Neumann,et al.  Pareto Optimization for Subset Selection with Dynamic Cost Constraints , 2018, AAAI.

[194]  Thomas Jansen,et al.  On the analysis of a dynamic evolutionary algorithm , 2006, J. Discrete Algorithms.

[195]  Yang Yu,et al.  Evolutionary Learning: Advances in Theories and Algorithms , 2019 .

[196]  Benjamin Doerr,et al.  Self-Adjusting Mutation Rates with Provably Optimal Success Rules , 2019, Algorithmica.