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Dynamic parameter choices in evolutionary computation

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  1. {AAD+13} Peyman Afshani, Manindra Agrawal, Benjamin Doerr, Carola Doerr, Kasper Green Larsen, and Kurt Mehlhorn. The query complexity of finding a hidden permutation. In Space-Efficient Data Structures, Streams, and Algorithms - Papers in Honor of J. Ian Munro on the Occasion of His 66th Birthday, volume 8066 of Lecture Notes in Computer Science, pages 1--11. Springer, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  2. {ACBF02} Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47:235--256, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. {AM16} Aldeida Aleti and Irene Moser. A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Computing Surveys, 49:56:1--56:35, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. {AMM94} Jaroslaw Arabas, Zbigniew Michalewicz, and Jan J. Mulawka. GAVaPS - A genetic algorithm with varying population size. In Proc. of International Conference on Evolutionary Computation (ICEC'94), pages 73--78. IEEE, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  5. {AMS<sup>+</sup>15} Carlos Ansótegui, Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, and Kevin Tierney. Model-based genetic algorithms for algorithm configuration. In Proc. of International Conference on Artificial Intelligence (IJCAI'15), pages 733--739. AAAI Press, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. {Aug09} Anne Auger. Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed. In Companion Material for Proc. of Genetic and Evolutionary Computation Conference (GECCO'09), pages 2447--2452. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. {Bäc92} Thomas Bäck. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm. In Proc. of Parallel Problem Solving from Nature (PPSN'92), pages 87--96. Elsevier, 1992.Google ScholarGoogle Scholar
  8. {Bäc96} Thomas Bäck. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, 1996. Google ScholarGoogle ScholarCross RefCross Ref
  9. {BBFKK10} Thomas Bartz-Beielstein, Oliver Flasch, Patrick Koch, and Wolfgang Konen. SPOT: A toolbox for interactive and automatic tuning in the R environment. In Proc. of the 20. Workshop Computational Intelligence, pages 264--273. Universitätsverlag Karlsruhe, 2010.Google ScholarGoogle Scholar
  10. {BD17} Maxim Buzdalov and Benjamin Doerr. Runtime analysis of the (1 + (λ, λ)) Genetic Algorithm on random satisfiable 3-CNF formulas. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'17), pages 1343--1350. ACM, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. {BD19} Nathan Buskulic and Carola Doerr. Maximizing drift is not optimal for solving onemax. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'19, Companion). ACM, 2019. Full version available online at http://arxiv.org/abs/1904.07818. See also https://github.com/NathanBuskulic/OneMaxOptimal for more project data. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. {BDN10} Süntje Böttcher, Benjamin Doerr, and Frank Neumann. Optimal fixed and adaptive mutation rates for the LeadingOnes problem. In Proc. of Parallel Problem Solving from Nature (PPSN'10), volume 6238 of Lecture Notes in Computer Science, pages 1--10. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. {BeS00} Helio J.C. Barbosa and Asla Medeiros e Sá. On adaptive operator probabilities in real coded genetic algorithms. In Proc. of Conference of the Chilean Computer Science Society, 2000.Google ScholarGoogle Scholar
  14. {BEvdV00} Thomas Bäck, A. E. Eiben, and Nikolai A. L. van der Vaart. An empirical study on gas "without parameters". In Proc. of Parallel Problem Solving from Nature (PPSN'00), volume 1917 of Lecture Notes in Computer Science, pages 315--324. Springer, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. {BLS14} Golnaz Badkobeh, Per Kristian Lehre, and Dirk Sudholt. Unbiased black-box complexity of parallel search. In Proc. of Parallel Problem Solving from Nature (PPSN'14), volume 8672 of Lecture Notes in Computer Science, pages 892--901. Springer, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  16. {CFSS08} Luís Da Costa, Alvaro Fialho, Marc Schoenauer, and Michèle Sebag. Adaptive operator selection with dynamic multi-armed bandits. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'08), pages 913--920. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. {CS09} Jorge Cervantes and Christopher R. Stephens. Limitations of existing mutation rate heuristics and how a rank GA overcomes them. IEEE Transactions on Evolutionary Computation, 13:369--397, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. {CTR99} Carlos J. Costa, R. Tavares, and A. Rosa. An experimental study on dynamic random variation of population size. In Proc. of Systems, Man, and Cybernetics (SMC'99), pages 607--612. IEEE, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  19. {Dav89} Lawrence Davis. Adapting operator probabilities in genetic algorithms. In Proc. of International Conference on Genetic Algorithms (ICGA'89), pages 61--69. Morgan Kaufmann, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. {DD15a} Benjamin Doerr and Carola Doerr. Optimal parameter choices through self-adjustment: Applying the 1/5-th rule in discrete settings. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'15), pages 1335--1342. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. {DD15b} Benjamin Doerr and Carola Doerr. A tight runtime analysis of the (1+(λ,λ)) genetic algorithm on OneMax. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'15), pages 1423--1430. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. {DD18a} Benjamin Doerr and Carola Doerr. Optimal static and self-adjusting parameter choices for the (1+ (λ, λ)) genetic algorithm. Algorithmica, 80:1658--1709, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. {DD18b} Benjamin Doerr and Carola Doerr. Theory of parameter control mechanisms for discrete black-box optimization: Provable performance gains through dynamic parameter choices. In Benjamin Doerr and Frank Neumann, editors, Theory of Randomized Search Heuristics in Discrete Search Spaces. Springer, 2018. To appear. Available online at https://arxiv.org/abs/1804.05650.Google ScholarGoogle Scholar
  24. {DD19} Nguyen Dang and Carola Doerr. Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'19). ACM, 2019. Full version available online at https://arxiv.org/abs/1904.04608. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. {DDE13} Benjamin Doerr, Carola Doerr, and Franziska Ebel. Lessons from the black-box: Fast crossover-based genetic algorithms. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'13), pages 781--788. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. {DDE15} Benjamin Doerr, Carola Doerr, and Franziska Ebel. From black-box complexity to designing new genetic algorithms. Theoretical Computer Science, 567:87--104, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. {DDK18} Benjamin Doerr, Carola Doerr, and Timo Kötzing. Static and self-adjusting mutation strengths for multi-valued decision variables. Algorithmica, 80:1732--1768, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. {DDY16a} Benjamin Doerr, Carola Doerr, and Jing Yang. k-bit mutation with self-adjusting k outperforms standard bit mutation. In Proc. of Parallel Problem Solving from Nature (PPSN'16), volume 9921 of Lecture Notes in Computer Science, pages 824--834. Springer, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  29. {DDY16b} Benjamin Doerr, Carola Doerr, and Jing Yang. Optimal parameter choices via precise black-box analysis. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'16), pages 1123--1130. ACM, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. {Dev72} Luc Devroye. The compound random search. Ph.D. dissertation, Purdue Univ., West Lafayette, IN, 1972.Google ScholarGoogle Scholar
  31. {DGWY17} Benjamin Doerr, Christian Gießen, Carsten Witt, and Jing Yang. The (1 + λ) evolutionary algorithm with self-adjusting mutation rate. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'17), pages 1351--1358. ACM, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. {DJ75} Kenneth Alan De Jong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan, Ann Arbor, MI, USA, 1975.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. {DJS+13} Benjamin Doerr, Thomas Jansen, Dirk Sudholt, Carola Winzen, and Christine Zarges. Mutation rate matters even when optimizing monotonic functions. Evolutionary Computation, 21:1--27, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. {DL16} Duc-Cuong Dang and Per Kristian Lehre. Self-adaptation of mutation rates in non-elitist populations. In Proc. of Parallel Problem Solving from Nature (PPSN'16), volume 9921 of LNCS, pages 803--813. Springer, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  35. {DL19} Benjamin Doerr and Carola Doerr Johannes Lengler. Self-adjusting mutation rates with provably optimal success rules. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'19). ACM, 2019. Full version available online at http://arxiv.org/abs/1902.02588. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. {DLOW18} Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto, and John Alasdair Warwicker. On the runtime analysis of selection hyper-heuristics with adaptive learning periods. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'18), pages 1015--1022. ACM, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. {Doe16} Benjamin Doerr. Optimal parameter settings for the (1 + (λ, λ)) genetic algorithm. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'16), pages 1107--1114. ACM, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. {Doe18} Benjamin Doerr. Better runtime guarantees via stochastic domination. In Proc. of Evolutionary Computation in Combinatorial Optimization (EvoCOP'18), volume 10782 of Lecture Notes in Computer Science, pages 1--17. Springer, 2018. Full version available at http://arxiv.org/abs/1801.04487.Google ScholarGoogle ScholarCross RefCross Ref
  39. {DW18} Carola Doerr and Markus Wagner. On the effectiveness of simple success-based parameter selection mechanisms for two classical discrete black-box optimization benchmark problems. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'18), pages 943--950. ACM, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. {DWY18} Benjamin Doerr, Carsten Witt, and Jing Yang. Runtime analysis for self-adaptive mutation rates. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'18), pages 1475--1482. ACM, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. {DYvR+18} Carola Doerr, Furong Ye, Sander van Rijn, Hao Wang, and Thomas Bäck. Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'18), pages 951--958. ACM, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. {EHM99} Agoston Endre Eiben, Robert Hinterding, and Zbigniew Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3:124--141, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. {EMSS07} A. E. Eiben, Zbigniew Michalewicz, Marc Schoenauer, and James E. Smith. Parameter control in evolutionary algorithms. In Parameter Setting in Evolutionary Algorithms, volume 54 of Studies in Computational Intelligence, pages 19--46. Springer, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  44. {EMV04} A. E. Eiben, Elena Marchiori, and V. A. Valkó. Evolutionary algorithms with on-the-fly population size adjustment. In Proc. of Parallel Problem Solving from Nature (PPSN'04), volume 3242 of Lecture Notes in Computer Science, pages 41--50. Springer, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  45. {ES11} A. E. Eiben and Selmar K. Smit. Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1:19--31, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  46. {FCSS08} Álvaro Fialho, Luís Da Costa, Marc Schoenauer, and Michèle Sebag. Extreme value based adaptive operator selection. In Proc. of Parallel Problem Solving from Nature (PPSN'08), volume 5199 of Lecture Notes in Computer Science, pages 175--184. Springer, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  47. {FCSS10} Álvaro Fialho, Luís Da Costa, Marc Schoenauer, and Michèle Sebag. Analyzing bandit-based adaptive operator selection mechanisms. Annals of Mathematics and Artificial Intelligence, 60:25--64, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. {GP14} Brian W. Goldman and William F. Punch. Parameter-less population pyramid. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'14), pages 785--792. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. {GP15} Brian W. Goldman and William F. Punch. Fast and efficient black box optimization using the parameter-less population pyramid. Evolutionary Computation, 23:451--479, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. {Gre86} John J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Trans. Systems, Man, and Cybernetics, 16:122--128, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. {GS16} Brian W. Goldman and Dirk Sudholt. Runtime analysis for the parameter-less population pyramid. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'160, pages 669--676. ACM, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. {GW16} Christian Gießen and Carsten Witt. Optimal mutation rates for the (1+λ) EA on OneMax. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'16), pages 1147--1154. ACM, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. {GW17} Christian Gießen and Carsten Witt. The interplay of population size and mutation probability in the (1+λ) EA on OneMax. Algorithmica, 78:587--609, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. {HHB10} Ting Hu, Simon Harding, and Wolfgang Banzhaf. Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm. Genetic Programming and Evolvable Machines, 11:205--225, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. {HHLB11} Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Proc. of Learning and Intelligent Optimization (LION'11), pages 507--523. Springer, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. {HHLBS09} Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, and Thomas Stützle. ParamILS: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research, 36:267--306, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. {HL99} Georges R. Harik and Fernando G. Lobo. A parameter-less genetic algorithm. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'99), pages 258--265. ACM, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. {HM90} Jürgen Hesser and Reinhard Männer. Towards an optimal mutation probability for genetic algorithms. In Proc. of Parallel Problem Solving from Nature (PPSN'90), volume 496 of Lecture Notes in Computer Science, pages 23--32. Springer, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. {JDJW05} Thomas Jansen, Kenneth A. De Jong, and Ingo Wegener. On the choice of the offspring population size in evolutionary algorithms. Evolutionary Computation, 13:413--440, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. {Jul95} Bryant A. Julstrom. What have you done for me lately? adapting operator probabilities in a steady-state genetic algorithm. In Proc. of International Conference on Genetic Algorithms (ICGA'95), pages 81--87. Morgan Kaufmann, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. {JW06} Thomas Jansen and Ingo Wegener. On the analysis of a dynamic evolutionary algorithm. Journal of Discrete Algorithms, 4:181--199, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  62. {KHE15} Giorgos Karafotias, Mark Hoogendoorn, and A.E. Eiben. Parameter control in evolutionary algorithms: Trends and challenges. IEEE Transactions on Evolutionary Computation, 19:167--187, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. {KK06} V. K. Koumousis and C. P. Katsaras. A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Transactions on Evolutionary Computation, 10:19--28, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. {KMH+04} Stefan Kern, Sibylle D. Müller, Nikolaus Hansen, Dirk Büche, Jiri Ocenasek, and Petros Koumoutsakos. Learning probability distributions in continuous evolutionary algorithms - a comparative review. Natural Computing, 3:77--112, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. {KS00} Natalio Krasnogor and Jim Smith. A memetic algorithm with self-adaptive local search: TSP as a case study. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'00), pages 987--994. Morgan Kaufmann, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. {LDC+16} Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Leslie Pérez Cáceres, Mauro Birattari, and Thomas Stützle. The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3:43--58, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  67. {Len18} Johannes Lengler. A general dichotomy of evolutionary algorithms on monotone functions. In Proc. of Parallel Problem Solving from Nature (PPSN'18), volume 11102 of Lecture Notes in Computer Science, pages 3--15. Springer, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  68. {LLM07} Fernando G. Lobo, Cláudio F. Lima, and Zbigniew Michalewicz, editors. Parameter Setting in Evolutionary Algorithms, volume 54 of Studies in Computational Intelligence. Springer, 2007. Google ScholarGoogle ScholarCross RefCross Ref
  69. {LOW17} Andrei Lissovoi, Pietro S. Oliveto, and John Alasdair Warwicker. On the runtime analysis of generalised selection hyper-heuristics for pseudo-Boolean optimisation. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'17), pages 849--856. ACM, 2017. Extended version available at https://arxiv.org/abs/1801.07546. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. {LS11} Jörg Lässig and Dirk Sudholt. Adaptive population models for offspring populations and parallel evolutionary algorithms. In Proc. of Foundations of Genetic Algorithms (FOGA'11), pages 181--192. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. {LS16} Johannes Lengler and Angelika Steger. Drift analysis and evolutionary algorithms revisited. CoRR, abs/1608.03226, 2016.Google ScholarGoogle Scholar
  72. {Müh92} Heinz Mühlenbein. How genetic algorithms really work: Mutation and hillclimbing. In Proc. of Parallel Problem Solving from Nature (PPSN'92), pages 15--26. Elsevier, 1992.Google ScholarGoogle Scholar
  73. {OLN09} Pietro Simone Oliveto, Per Kristian Lehre, and Frank Neumann. Theoretical analysis of rank-based mutation - combining exploration and exploitation. In Proc. of Congress on Evolutionary Computation (CEC'09), pages 1455--1462. IEEE, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. {RABD19} Anna Rodionova, Kirill Antonov, Arina Buzdalova, and Carola Doerr. Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'19). ACM, 2019. Full version available online at https://arxiv.org/abs/1904.08032. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. {Rec73} Ingo Rechenberg. Evolutionsstrategie. Friedrich Fromman Verlag (Günther Holzboog KG), Stuttgart, 1973.Google ScholarGoogle Scholar
  76. {Smi12} Selmar K. Smit. Parameter Tuning and Scientific Testing in Evolutionary Algorithms. PhD thesis, VU University of Amsterdam, 2012. PhD thesis.Google ScholarGoogle Scholar
  77. {SS68} Michael A. Schumer and Kenneth Steiglitz. Adaptive step size random search. IEEE Transactions on Automatic Control, 13:270--276, 1968.Google ScholarGoogle ScholarCross RefCross Ref
  78. {Thi05} Dirk Thierens. An adaptive pursuit strategy for allocating operator probabilities. In Proc. of Genetic and Evolutionary Computation Conference (GECCO'05), pages 1539--1546. ACM, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. {TR98} Andrew Tuson and Peter Ross. Adapting operator settings in genetic algorithms. Evolutionary Computation, 6:161--184, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  80. {Wit13} Carsten Witt. Tight bounds on the optimization time of a randomized search heuristic on linear functions. Combinatorics, Probability & Computing, 22:294--318, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  81. {WM97} D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1:67--82, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. {YDB19} Furong Ye, Carola Doerr, and Thomas Bäck. Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation. In Proc. Conference on Evolutionary Computation (CEC'19). IEEE, 2019. To appear. Available online at http://arxiv.org/abs/1901.05573.Google ScholarGoogle ScholarCross RefCross Ref
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      cover image ACM Conferences
      GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2019
      2161 pages
      ISBN:9781450367486
      DOI:10.1145/3319619

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