The Runtime of the Compact Genetic Algorithm on Jump Functions

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

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

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

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

[5]  Denis Antipov,et al.  Runtime Analysis of a Heavy-Tailed $(1+(\lambda,\lambda))$ Genetic Algorithm on Jump Functions , 2020 .

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

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

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

[9]  Benjamin Doerr,et al.  The $(1 + (\lambda, \lambda))$ GA Is Even Faster on Multimodal Problems , 2020 .

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

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

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

[13]  Benjamin Doerr,et al.  Probabilistic Tools for the Analysis of Randomized Optimization Heuristics , 2018, Theory of Evolutionary Computation.

[14]  Johannes Lengler,et al.  Drift Analysis , 2017, Theory of Evolutionary Computation.

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

[16]  Benjamin Doerr,et al.  Sharp Bounds for Genetic Drift in EDAs , 2019, ArXiv.

[17]  Aishwaryaprajna,et al.  The benefits and limitations of voting mechanisms in evolutionary optimisation , 2019, FOGA '19.

[18]  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.

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

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

[21]  Benjamin Doerr,et al.  The Efficiency Threshold for the Offspring Population Size of the ($\mu$, $\lambda$) EA , 2019, 1904.06981.

[22]  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.

[23]  Kurt Mehlhorn,et al.  The Query Complexity of a Permutation-Based Variant of Mastermind , 2019, Discret. Appl. Math..

[24]  Anirban Mukhopadhyay,et al.  Exploration and Exploitation Without Mutation: Solving the Jump Function in \varTheta (n) Time , 2018, PPSN.

[25]  C. Witt,et al.  On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization , 2018, Algorithmica.

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

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

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

[29]  Carsten Witt,et al.  Domino convergence: why one should hill-climb on linear functions , 2018, GECCO.

[30]  Weijie Zheng,et al.  Working principles of binary differential evolution , 2018, GECCO.

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

[32]  Benjamin Doerr,et al.  Precise Runtime Analysis for Plateaus , 2018, PPSN.

[33]  Pietro Simone Oliveto,et al.  Fast Artificial Immune Systems , 2018, PPSN.

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

[35]  Benjamin Doerr,et al.  Better Runtime Guarantees via Stochastic Domination , 2018, EvoCOP.

[36]  C. Witt Upper Bounds on the Running Time of the Univariate Marginal Distribution Algorithm on OneMax , 2017, Algorithmica.

[37]  Pietro Simone Oliveto,et al.  On the runtime analysis of the opt-IA artificial immune system , 2017, GECCO.

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

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

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

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

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

[43]  C. Witt,et al.  The Interplay of Population Size and Mutation Probability in the ($$1+\lambda $$1+λ) EA on OneMax , 2017, Algorithmica.

[44]  Benjamin Doerr,et al.  The Unrestricted Black-Box Complexity of Jump Functions , 2016, Evolutionary Computation.

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

[46]  Frank Neumann,et al.  Fast Building Block Assembly by Majority Vote Crossover , 2016, GECCO.

[47]  Dirk Sudholt,et al.  Update Strength in EDAs and ACO: How to Avoid Genetic Drift , 2016, GECCO.

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

[49]  Martin Pelikan,et al.  Estimation of Distribution Algorithms , 2015, Handbook of Computational Intelligence.

[50]  Thomas Jansen,et al.  Analyzing Evolutionary Algorithms , 2015, Natural Computing Series.

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

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

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

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

[55]  Kurt Mehlhorn,et al.  The Query Complexity of Finding a Hidden Permutation , 2013, Space-Efficient Data Structures, Streams, and Algorithms.

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

[57]  A. Auger,et al.  Theory of Randomized Search Heuristics , 2012, Algorithmica.

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

[59]  Benjamin Doerr,et al.  Tight Analysis of the (1+1)-EA for the Single Source Shortest Path Problem , 2011, Evolutionary Computation.

[60]  Anne Auger,et al.  Theory of Randomized Search Heuristics: Foundations and Recent Developments , 2011, Theory of Randomized Search Heuristics.

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

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

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

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

[65]  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.

[66]  R. Paul Wiegand,et al.  Black-box search by elimination of fitness functions , 2009, FOGA '09.

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

[68]  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 .

[69]  Ingo Wegener,et al.  Simulated Annealing Beats Metropolis in Combinatorial Optimization , 2005, ICALP.

[70]  Thomas Jansen,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 Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization , 2004 .

[71]  Ingo Wegener,et al.  Searching Randomly for Maximum Matchings , 2004, Electron. Colloquium Comput. Complex..

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

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

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

[75]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

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

[77]  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).

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

[79]  W. Hoeffding Probability inequalities for sum of bounded random variables , 1963 .