A gentle introduction to theory (for non-theoreticians)

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org GECCO ’21 Companion, Lille, France © 2021 Copyright held by the owner/author. Publication rights licensed to ACM. ISBN 978-1-4503-8351-6/21/07 $15.00 DOI 10.1145/3449726.3461406 Benjamin Doerr: A gentle introduction to theory Instructor: Benjamin Doerr

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

[2]  Benjamin Doerr,et al.  Choosing the right algorithm with hints from complexity theory: (hot-off-the-press track at GECCO 2022) , 2021, IJCAI.

[3]  Benjamin Doerr,et al.  A rigorous runtime analysis of the 2-MMASib on jump functions: ant colony optimizers can cope well with local optima , 2021, GECCO.

[4]  Benjamin Doerr,et al.  Generalized jump functions , 2021, GECCO.

[5]  Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution , 2021, GECCO.

[6]  C. Witt,et al.  Stagnation detection in highly multimodal fitness landscapes , 2021, GECCO.

[7]  Benjamin Doerr,et al.  Lower bounds from fitness levels made easy , 2021, GECCO.

[8]  Andrew M. Sutton.  Fixed-Parameter Tractability of Crossover: Steady-State GAs on the Closest String Problem , 2021, Algorithmica.

[9]  C. Witt,et al.  Stagnation Detection with Randomized Local Search* , 2021, Evolutionary Computation.

[10]  Benjamin Doerr,et al.  Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives. , 2020, Evolutionary computation.

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

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

[13]  Benjamin Doerr,et al.  A Simplified Run Time Analysis of the Univariate Marginal Distribution Algorithm on LeadingOnes , 2020, Theor. Comput. Sci..

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

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

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

[17]  Dirk Sudholt,et al.  The Complex Parameter Landscape of the Compact Genetic Algorithm , 2020, Algorithmica.

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

[19]  Dirk Sudholt,et al.  A tight lower bound on the expected runtime of standard steady state genetic algorithms , 2020, GECCO.

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

[21]  Carsten Witt,et al.  Improved Fixed-Budget Results via Drift Analysis , 2020, PPSN.

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

[23]  C. Witt,et al.  Evolutionary Algorithms with Self-adjusting Asymmetric Mutation , 2020, PPSN.

[24]  Johannes Lengler,et al.  Large Population Sizes and Crossover Help in Dynamic Environments , 2020, PPSN.

[25]  Benjamin Doerr,et al.  Fixed-Target Runtime Analysis , 2020, GECCO.

[26]  Maxim Buzdalov,et al.  The (1 + (λ, λ)) genetic algorithm for permutations , 2020, GECCO Companion.

[27]  Benjamin Doerr,et al.  From understanding genetic drift to a smart-restart parameter-less compact genetic algorithm , 2020, GECCO.

[28]  Benjamin Doerr,et al.  The (1 + (λ,λ)) GA is even faster on multimodal problems , 2020, GECCO.

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

[30]  Benjamin Doerr,et al.  Exponential Upper Bounds for the Runtime of Randomized Search Heuristics , 2020, PPSN.

[31]  Benjamin Doerr.  Does Comma Selection Help to Cope with Local Optima? , 2020, GECCO.

[32]  Per Kristian Lehre,et al.  Self-Adaptation in Nonelitist Evolutionary Algorithms on Discrete Problems With Unknown Structure , 2020, IEEE Transactions on Evolutionary Computation.

[33]  Dirk Sudholt,et al.  On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm , 2020, GECCO.

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

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

[36]  Pietro Simone Oliveto,et al.  On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms , 2019, Algorithmica.

[37]  Dirk Sudholt,et al.  Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial , 2018, Algorithmica.

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

[39]  Benjamin Doerr,et al.  Runtime Analysis for Self-adaptive Mutation Rates , 2018, Algorithmica.

[40]  Benjamin Doerr,et al.  Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices , 2018, Theory of Evolutionary Computation.

[41]  Johannes Lengler,et al.  A General Dichotomy of Evolutionary Algorithms on Monotone Functions , 2018, IEEE Transactions on Evolutionary Computation.

[42]  Thomas Bäck,et al.  Theory of Evolutionary Computation: Recent Developments in Discrete Optimization , 2020, Theory of Evolutionary Computation.

[43]  Dirk Sudholt,et al.  Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem , 2019, FOGA '19.

[44]  Benjamin Doerr,et al.  A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones , 2019, FOGA '19.

[45]  Johannes Lengler,et al.  Exponential slowdown for larger populations: the (µ + 1)-EA on monotone functions , 2019, FOGA '19.

[46]  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.

[47]  Andrei Lissovoi,et al.  On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation , 2019, AAAI.

[48]  Benjamin Doerr,et al.  Theoretical and empirical study of the (1 + (λ, λ)) EA on the leadingones problem , 2019, GECCO.

[49]  Andrew M. Sutton,et al.  When resampling to cope with noise, use median, not mean , 2019, GECCO.

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

[51]  Dirk Sudholt,et al.  On the benefits and risks of using fitness sharing for multimodal optimisation , 2019, Theor. Comput. Sci..

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

[53]  Maxim Buzdalov,et al.  The 1/5-th rule with rollbacks: on self-adjustment of the population size in the (1 + (λ, λ)) GA , 2019, GECCO.

[54]  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.

[55]  Frank Neumann,et al.  Fast re-optimization via structural diversity , 2019, GECCO.

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

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

[58]  Benjamin Doerr,et al.  The ($$1+\lambda $$1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate , 2018, Algorithmica.

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

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

[61]  Duc-Cuong Dang,et al.  Level-Based Analysis of the Univariate Marginal Distribution Algorithm , 2018, Algorithmica.

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

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

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

[65]  Angelika Steger,et al.  The linear hidden subset problem for the (1 + 1) EA with scheduled and adaptive mutation rates , 2018, GECCO.

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

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

[68]  Per Kristian Lehre,et al.  University of Birmingham Level-based analysis of the population-based incremental learning algorithm , 2018 .

[69]  Per Kristian Lehre,et al.  Escaping Local Optima Using Crossover With Emergent Diversity , 2018, IEEE Transactions on Evolutionary Computation.

[70]  Dirk Sudholt,et al.  Runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation , 2018, GECCO.

[71]  Andrei Lissovoi,et al.  Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes* , 2018, Evolutionary Computation.

[72]  Dogan Corus,et al.  Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms , 2017, IEEE Transactions on Evolutionary Computation.

[73]  Barbara Geissmann,et al.  Sorting by Swaps with Noisy Comparisons , 2017, Algorithmica.

[74]  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.

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

[76]  OneMax,et al.  EA on Generalized Dynamic OneMax , 2018 .

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

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

[79]  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.

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

[81]  Benjamin Doerr,et al.  Unknown solution length problems with no asymptotically optimal run time , 2017, GECCO.

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

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

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

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

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

[87]  Dirk Sudholt,et al.  Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem , 2016, Evolutionary Computation.

[88]  Dirk Sudholt,et al.  How Crossover Speeds up Building Block Assembly in Genetic Algorithms , 2014, Evolutionary Computation.

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

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

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

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

[93]  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.

[94]  Duc-Cuong Dang,et al.  Escaping Local Optima with Diversity Mechanisms and Crossover , 2016, GECCO.

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

[96]  Duc-Cuong Dang,et al.  Populations Can Be Essential in Tracking Dynamic Optima , 2016, Algorithmica.

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

[98]  Benjamin Doerr,et al.  Optimal Parameter Settings for the (1 + λ, λ) Genetic Algorithm , 2016, GECCO.

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

[100]  Per Kristian Lehre,et al.  A Parameterised Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms , 2014, Evolutionary Computation.

[101]  Duc-Cuong Dang,et al.  Runtime Analysis of Non-elitist Populations: From Classical Optimisation to Partial Information , 2016, Algorithmica.

[102]  Frank Neumann,et al.  Maximizing Submodular Functions under Matroid Constraints by Evolutionary Algorithms , 2015, Evolutionary Computation.

[103]  Olivier Teytaud,et al.  Analysis of runtime of optimization algorithms for noisy functions over discrete codomains , 2015, Theor. Comput. Sci..

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

[105]  Benjamin Doerr,et al.  Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization , 2015, GECCO.

[106]  Benjamin Doerr,et al.  A Tight Runtime Analysis of the (1+(λ, λ)) Genetic Algorithm on OneMax , 2015, GECCO.

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

[108]  Benjamin Doerr,et al.  Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings , 2015, GECCO.

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

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

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

[112]  Johannes Lengler,et al.  Fixed Budget Performance of the (1+1) EA on Linear Functions , 2015, FOGA.

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

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

[115]  Timo Kötzing,et al.  Robustness of Populations in Stochastic Environments , 2014, Algorithmica.

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

[117]  Benjamin Doerr,et al.  The unbiased black-box complexity of partition is polynomial , 2014, Artif. Intell..

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

[119]  Thomas Jansen,et al.  Reevaluating Immune-Inspired Hypermutations Using the Fixed Budget Perspective , 2014, IEEE Transactions on Evolutionary Computation.

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

[121]  Thomas Jansen,et al.  Performance analysis of randomised search heuristics operating with a fixed budget , 2014, Theor. Comput. Sci..

[122]  William F. Punch,et al.  Parameter-less population pyramid , 2014, GECCO.

[123]  Carsten Witt,et al.  Revised analysis of the (1+1) ea for the minimum spanning tree problem , 2014, GECCO.

[124]  Per Kristian Lehre,et al.  Runtime analysis of selection hyper-heuristics with classical learning mechanisms , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[125]  Benjamin Doerr,et al.  Unbiased black-box complexities of jump functions: how to cross large plateaus , 2014, GECCO.

[126]  Benjamin Doerr,et al.  Lessons from the black-box: fast crossover-based genetic algorithms , 2013, GECCO '13.

[127]  Thomas Jansen,et al.  A method to derive fixed budget results from expected optimisation times , 2013, GECCO '13.

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

[129]  Dirk Sudholt,et al.  When do evolutionary algorithms optimize separable functions in parallel? , 2013, FOGA XII '13.

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

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

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

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

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

[135]  Thomas Jansen,et al.  Analyzing Evolutionary Algorithms: The Computer Science Perspective , 2012 .

[136]  Thomas Jansen,et al.  Fixed budget computations: a different perspective on run time analysis , 2012, GECCO '12.

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

[138]  Benjamin Doerr,et al.  Playing Mastermind With Many Colors , 2012, SODA.

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

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

[141]  Benjamin Doerr,et al.  Reducing the arity in unbiased black-box complexity , 2012, GECCO '12.

[142]  Benjamin Doerr,et al.  Ranking-Based Black-Box Complexity , 2011, Algorithmica.

[143]  X. Yao,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 On the Impact of Mutation-Selection Balance on the Runtime of , 2022 .

[144]  Benjamin Doerr,et al.  Memory-restricted black-box complexity of OneMax , 2012, Inf. Process. Lett..

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

[146]  Per Kristian Lehre,et al.  Crossover can be constructive when computing unique input–output sequences , 2011, Soft Comput..

[147]  Dirk Sudholt,et al.  How crossover helps in pseudo-boolean optimization , 2011, GECCO '11.

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

[149]  Tom Schaul,et al.  High dimensions and heavy tails for natural evolution strategies , 2011, GECCO '11.

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

[151]  Per Kristian Lehre,et al.  Faster black-box algorithms through higher arity operators , 2010, FOGA '11.

[152]  Dirk Sudholt,et al.  Analysis of an Iterated Local Search Algorithm for Vertex Coloring , 2010, ISAAC.

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

[154]  Frank Neumann,et al.  How Crossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem , 2010, PPSN.

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

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

[157]  Frank Neumann,et al.  More Effective Crossover Operators for the All-Pairs Shortest Path Problem , 2010, PPSN.

[158]  Per Kristian Lehre,et al.  Black-Box Search by Unbiased Variation , 2010, GECCO '10.

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

[160]  Petr Posík,et al.  Comparison of cauchy EDA and BIPOP-CMA-ES algorithms on the BBOB noiseless testbed , 2010, GECCO '10.

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

[162]  Pietro Simone Oliveto,et al.  Analysis of the $(1+1)$-EA for Finding Approximate Solutions to Vertex Cover Problems , 2009, IEEE Transactions on Evolutionary Computation.

[163]  Benjamin Doerr,et al.  Improved analysis methods for crossover-based algorithms , 2009, GECCO.

[164]  Petr Posík,et al.  BBOB-benchmarking a simple estimation of distribution algorithm with cauchy distribution , 2009, GECCO '09.

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

[166]  Frank Neumann,et al.  Comparison of simple diversity mechanisms on plateau functions , 2009, Theor. Comput. Sci..

[167]  Frank Neumann,et al.  Analyses of Simple Hybrid Algorithms for the Vertex Cover Problem , 2009, Evolutionary Computation.

[168]  Frank Neumann,et al.  Computing single source shortest paths using single-objective fitness , 2009, FOGA '09.

[169]  Tobias Storch,et al.  On the Choice of the Parent Population Size , 2008, Evolutionary Computation.

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

[171]  Alden H. Wright,et al.  Ignoble Trails - Where Crossover Is Provably Harmful , 2008, PPSN.

[172]  Benjamin Doerr,et al.  Crossover can provably be useful in evolutionary computation , 2008, GECCO '08.

[173]  Frank Neumann,et al.  Rigorous analyses of fitness-proportional selection for optimizing linear functions , 2008, GECCO '08.

[174]  Frank Neumann,et al.  Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators , 2007, Evolutionary Computation.

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

[176]  Frank Neumann,et al.  Rigorous analyses of simple diversity mechanisms , 2007, GECCO '07.

[177]  Benjamin Doerr,et al.  Adjacency list matchings: an ideal genotype for cycle covers , 2007, GECCO '07.

[178]  Thomas Jansen,et al.  A building-block royal road where crossover is provably essential , 2007, GECCO '07.

[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.  Faster Evolutionary Algorithms by Superior Graph Representation , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

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

[182]  Benjamin Doerr,et al.  A Tight Bound for the (1+1)-EA on the Single Source Shortest Path Problem , 2007 .

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

[184]  Anne Auger,et al.  When Do Heavy-Tail Distributions Help? , 2006, PPSN.

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

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

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

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

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

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

[191]  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 .

[192]  Ingo Wegener,et al.  The one-dimensional Ising model: Mutation versus recombination , 2005, Theor. Comput. Sci..

[193]  Thomas Jansen,et al.  Theoretical analysis of a mutation-based evolutionary algorithm for a tracking problem in the lattice , 2005, GECCO '05.

[194]  Dirk Sudholt,et al.  Crossover is provably essential for the Ising model on trees , 2005, GECCO '05.

[195]  Carsten Witt,et al.  UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics , 2004 .

[196]  Ingo Wegener,et al.  Real royal road functions--where crossover provably is essential , 2001, Discret. Appl. Math..

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

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

[199]  Ingo Wegener,et al.  Randomized local search, evolutionary algorithms, and the minimum spanning tree problem , 2004, Theor. Comput. Sci..

[200]  Ingo Wegener,et al.  The Ising Model on the Ring: Mutation Versus Recombination , 2004, GECCO.

[201]  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 .

[202]  Frank Neumann,et al.  Expected runtimes of evolutionary algorithms for the Eulerian cycle problem , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[204]  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 .

[205]  Ingo Wegener,et al.  Real royal road functions for constant population size , 2003, Theor. Comput. Sci..

[206]  Ingo Wegener,et al.  Evolutionary Algorithms and the Maximum Matching Problem , 2003, STACS.

[207]  Thomas Jansen,et al.  The Analysis of Evolutionary Algorithms—A Proof That Crossover Really Can Help , 2002, Algorithmica.

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

[209]  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).

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

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

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

[213]  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 .

[214]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

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

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

[217]  Thomas Jansen,et al.  On the Optimization of Unimodal Functions with the (1 + 1) Evolutionary Algorithm , 1998, PPSN.

[218]  I. Wegener,et al.  A rigorous complexity analysis of the (1+1) evolutionary algorithm for linear functions with Boolean inputs , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[219]  David E. Goldberg,et al.  The compact genetic algorithm , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[220]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

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

[222]  Günter Rudolph,et al.  How Mutation and Selection Solve Long-Path Problems in Polynomial Expected Time , 1996, Evolutionary Computation.

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

[224]  Kalyanmoy Deb,et al.  Long Path Problems , 1994, PPSN.

[225]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[226]  H. Szu.  Fast simulated annealing , 1987 .

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