The (1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mu...
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| Published in | Algorithmica Vol. 81; no. 2; pp. 593 - 631 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
15.02.2019
Springer Nature B.V Springer Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0178-4617 1432-0541 |
| DOI | 10.1007/s00453-018-0502-x |
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| Abstract | We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the
(
1
+
λ
)
evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the
(
1
+
λ
)
EA finds the optimum in an expected optimization time (number of fitness evaluations) of
O
(
n
λ
/
log
λ
+
n
log
n
)
. This time is asymptotically smaller than the optimization time of the classic
(
1
+
λ
)
EA. Previous work shows that this performance is best-possible among all
λ
-parallel mutation-based unbiased black-box algorithms. This result shows that the new way of adjusting the mutation rate can find optimal dynamic parameter values on the fly. Since our adjustment mechanism is simpler than the ones previously used for adjusting the mutation rate and does not have parameters itself, we are optimistic that it will find other applications. |
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| AbstractList | We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the (1+λ) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the (1+λ) EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(nλ/logλ+nlogn). This time is asymptotically smaller than the optimization time of the classic (1+λ) EA. Previous work shows that this performance is best-possible among all λ-parallel mutation-based unbiased black-box algorithms. This result shows that the new way of adjusting the mutation rate can find optimal dynamic parameter values on the fly. Since our adjustment mechanism is simpler than the ones previously used for adjusting the mutation rate and does not have parameters itself, we are optimistic that it will find other applications. We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the ( 1 + λ ) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the ( 1 + λ ) EA finds the optimum in an expected optimization time (number of fitness evaluations) of O ( n λ / log λ + n log n ) . This time is asymptotically smaller than the optimization time of the classic ( 1 + λ ) EA. Previous work shows that this performance is best-possible among all λ -parallel mutation-based unbiased black-box algorithms. This result shows that the new way of adjusting the mutation rate can find optimal dynamic parameter values on the fly. Since our adjustment mechanism is simpler than the ones previously used for adjusting the mutation rate and does not have parameters itself, we are optimistic that it will find other applications. |
| Author | Witt, Carsten Gießen, Christian Yang, Jing Doerr, Benjamin |
| Author_xml | – sequence: 1 givenname: Benjamin surname: Doerr fullname: Doerr, Benjamin organization: École Polytechnique, CNRS, Laboratoire d’Informatique (LIX) – sequence: 2 givenname: Christian surname: Gießen fullname: Gießen, Christian organization: Division Chassis & Safety, Advanced Engineering, Continental – sequence: 3 givenname: Carsten surname: Witt fullname: Witt, Carsten email: cawi@dtu.dk organization: DTU Compute, Technical University of Denmark – sequence: 4 givenname: Jing surname: Yang fullname: Yang, Jing organization: École Polytechnique, CNRS, Laboratoire d’Informatique (LIX) |
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| Cites_doi | 10.1007/3-540-36494-3_37 10.1016/j.jda.2005.01.002 10.1007/978-3-319-45823-6_73 10.1109/TEVC.2012.2202241 10.1016/j.spl.2018.03.016 10.1145/1830483.1830749 10.1145/2739480.2754681 10.1145/2908812.2908891 10.1145/1967654.1967671 10.1017/S0963548309990599 10.1016/j.tcs.2014.03.015 10.1111/j.1467-9574.1980.tb00681.x 10.1142/7438 10.1016/j.tcs.2006.11.002 10.1007/978-3-319-45823-6_75 10.1007/11523468_48 10.1145/3071178.3071301 10.1145/2908812.2908885 10.1016/S0304-3975(01)00182-7 10.1145/3071178.3071233 10.1007/978-3-540-87700-4_12 10.1162/evco.1999.7.2.173 10.1108/17563780910959893 10.1145/2001576.2001856 10.1145/1389095.1389271 10.1109/CEC.2009.4983114 10.1145/2725494.2725502 10.1162/106365605774666921 10.1145/3071178.3071288 10.1162/evco_a_00212 10.1109/CEC.2014.6900602 10.1145/3205455.3205627 10.1145/1967654.1967670 10.1145/3205455.3205611 10.1145/2739480.2754684 10.1145/2908812.2908950 10.1109/4235.771166 10.1145/3071178.3071297 10.1007/978-3-319-10762-2_88 10.1016/j.tcs.2014.11.028 10.1007/978-3-642-15844-5_1 10.1007/978-3-642-17339-4 10.1145/1527125.1527132 10.1007/978-3-319-45823-6_77 10.1007/s00453-016-0214-z 10.1007/978-3-319-45823-6_78 10.1145/2460239.2460249 10.1145/3071178.3071279 10.1007/s00453-012-9622-x 10.1145/3205455.3205569 10.1007/978-3-319-13075-0_54 |
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| Keywords | Mutation Runtime analysis Self-adaptation Evolutionary computation |
| Language | English |
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| References | DrosteSJansenTWegenerIOn the analysis of the (1+1) evolutionary algorithmTheor. Comput. Sci.20022765181189634710.1016/S0304-3975(01)00182-71002.68037 EibenAEHinterdingRMichalewiczZParameter control in evolutionary algorithmsIEEE Trans. Evolut. Comput.1999312414110.1109/4235.771166 JansenTWegenerIOn the analysis of a dynamic evolutionary algorithmJ. Discrete Algorithms20064181199221174510.1016/j.jda.2005.01.0021128.68118 JansenTDe JongKAWegenerIOn the choice of the offspring population size in evolutionary algorithmsEvolut. Comput.20051341344010.1162/106365605774666921 Kötzing, T., Lissovoi, A., Witt, C.: (1+1) EA on generalized dynamic OneMax. In: Proceedings of FOGA ’15, pp. 40–51. ACM (2015) Giel, O., Wegener, I.: Evolutionary algorithms and the maximum matching problem. In: Proceedings of STACS ’03, pp. 415–426. Springer (2003) Doerr, B., Doerr, C., Kötzing, T.: Provably optimal self-adjusting step sizes for multi-valued decision variables. In: Proceedings of PPSN ’16, pp. 782–791. Springer (2016b) Doerr, B., Doerr, C., Kötzing, T.: Solving problems with unknown solution length at (almost) no extra cost. In: Proceedings of GECCO ’15, pp. 831–838. ACM (2015b) DoerrBAugerADoerrBAnalyzing randomized search heuristics: tools from probability theoryTheory of Randomized Search Heuristics2011SingaporeWorld Scientific Publishing120 DoerrBDoerrCEbelFFrom black-box complexity to designing new genetic algorithmsTheor. Comput. Sci.201556787104329562610.1016/j.tcs.2014.11.0281314.68290 RobbinsHA remark on Stirling’s formulaAm. Math. Mon.1955622629693280068.05404 Alanazi, F., Lehre, P.K.: Runtime analysis of selection hyper-heuristics with classical learning mechanisms. In: Proceedings of CEC ’14, pp. 2515–2523. IEEE (2014) Lehre, P.K., Witt, C.: Concentrated hitting times of randomized search heuristics with variable drift. In: Proceedings of ISAAC ’14, pp. 686–697. Springer (2014) DietzfelbingerMRoweJEWegenerIWoelfelPTight bounds for blind search on the integers and the realsComb. Probab. Comput.201019711728272607610.1017/S09635483099905991261.68069 DoerrBAn elementary analysis of the probability that a binomial random variable exceeds its expectationStat. Probab. Lett.20181396774380218510.1016/j.spl.2018.03.0161392.60022 SudholtDA new method for lower bounds on the running time of evolutionary algorithmsIEEE Trans. Evolut. Comput.20131741843510.1109/TEVC.2012.2202241 Zarges, C.: On the utility of the population size for inversely fitness proportional mutation rates. In: Proceedings of FOGA ’09, pp. 39–46. ACM (2009) MitavskiyBRoweJECanningsCTheoretical analysis of local search strategies to optimize network communication subject to preserving the total number of linksInt. J. Intell. Comput. Cybern.20092243284275150210.1108/175637809109598931175.90210 Zarges, C.: Rigorous runtime analysis of inversely fitness proportional mutation rates. In: Proceedings of PPSN ’08, pp. 112–122. Springer (2008) KaasRBuhrmanJMMean, median and mode in binomial distributionsStat. Neerl.198034131857600510.1111/j.1467-9574.1980.tb00681.x0444.62021 Doerr, B., Doerr, C., Kötzing, T.: Unknown solution length problems with no asymptotically optimal run time. In: Proceedings of GECCO ’17, pp. 1367–1374. ACM (2017a) GießenCWittCThe interplay of population size and mutation probability in 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 OneMaxAlgorithmica201778587609364191210.1007/s00453-016-0214-z1366.68262 Doerr, B., Doerr, C.: Optimal parameter choices through self-adjustment: applying the 1/5-th rule in discrete settings. In: Proceedings of GECCO ’15, pp. 1335–1342. ACM (2015) Auger, A., Doerr, B. (eds.): Theory of Randomized Search Heuristics. World Scientific Publishing, Singapore (2011) Doerr, B., Witt, C., Yang, J.: Runtime analysis for self-adaptive mutation rates. In: Proc. GECCO ’18, pp. 1475–1482. ACM (2018b) Buzdalov, M., Doerr, B.: Runtime analysis of 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}$$(1+(\lambda ,\lambda ))$$\end{document} genetic algorithm on random satisfiable 3-CNF formulas. In: Proceedings of GECCO ’17, pp. 1343–1350. ACM (2017) Böttcher, S., Doerr, B., Neumann, F.: Optimal fixed and adaptive mutation rates for the LeadingOnes problem. In: Proceedings of PPSN ’10, pp. 1–10. Springer (2010) Dang, D-C., Lehre, P.K.: Self-adaptation of mutation rates in non-elitist populations. In: Proceedings of PPSN ’16, pp. 803–813. Springer (2016) Doerr, B., Doerr, C., Kötzing, T.: The right mutation strength for multi-valued decision variables. In: Proceedings of GECCO ’16, pp. 1115–1122. ACM (2016a) HwangH-KPanholzerARolinNTsaiT-HChenW-MProbabilistic analysis of the (1+1)-evolutionary algorithmEvolut. Comput.20182629934510.1162/evco_a_00212 Doerr, B., Fouz, M., Witt, C.: Quasirandom evolutionary algorithms. In: Proceedings of GECCO ’10, pp. 1457–1464. ACM (2010) Cathabard, S., Lehre, P.K., Yao, X.: Non-uniform mutation rates for problems with unknown solution lengths. In: Proceedings of FOGA ’11, pp. 173–180. ACM (2011) Doerr, B., Lissovoi, A., Oliveto, P.S., Warwicker, J.A.: On the runtime analysis of selection hyper-heuristics with adaptive learning periods. In: Proceedings of GECCO ’18, pp. 1015–1022. ACM (2018a) Doerr, B., Doerr, C., Yang, J.: k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}-bit mutation with self-adjusting k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document} outperforms standard bit mutation. In: Proceedings of PPSN ’16, pp. 824–834. Springer (2016d) Badkobeh, G., Lehre, P.K., Sudholt, D.: Unbiased black-box complexity of parallel search. In: Proceedings of PPSN ’14, pp. 892–901. Springer (2014) Oliveto, P.S., Lehre, P.K., Neumann, F.: Theoretical analysis of rank-based mutation-combining exploration and exploitation. In: Proceedings of CEC ’09, pp. 1455–1462. IEEE (2009) Johannsen, D.: Random combinatorial structures and randomized search heuristics. Ph.D. thesis, Saarland University (2010) Doerr, B., Doerr, C., Yang, J.: Optimal parameter choices via precise black-box analysis. In: Proceedings of GECCO ’16, pp. 1123–1130. ACM (2016c) Lehre, P.K., Özcan, E.: A runtime analysis of simple hyper-heuristics: to mix or not to mix operators. In: Proceedings of FOGA ’13, pp. 97–104. ACM (2013) Doerr, B., Fouz, M., Witt, C.: Sharp bounds by probability-generating functions and variable drift. In: Proceedings of GECCO ’11, pp. 2083–2090. ACM (2011) DoerrBKünnemannMOptimizing linear functions with 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}) evolutionary algorithm—different asymptotic runtimes for different instancesTheor. Comput. Sci.2015561323328218110.1016/j.tcs.2014.03.0151303.68120 Cervantes, J., Stephens, C.R.: Rank based variation operators for genetic algorithms. In: Proceedings of GECCO ’08, pp. 905–912. ACM (2008) Qian, C., Tang, K., Zhou, Z-H.: Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization. In: Proceedings of PPSN ’16, pp. 835–846. Springer (2016) Lässig, J., Sudholt, D.: Adaptive population models for offspring populations and parallel evolutionary algorithms. In: Proceedings of FOGA ’11, pp. 181–192. ACM (2011) Antipov, D., Doerr, B., Fang, J., Hetet, T.: Runtime analysis for the (μ+λ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\mu +\lambda )$$\end{document} EA optimizing OneMax. In: Proceedings of GECCO ’18, pp. 1459–1466. ACM (2018) Wegener, I.: Simulated annealing beats Metropolis in combinatorial optimization. In: Proceedings of ICALP ’05, pp. 589–601. Springer (2005) GarnierJKallelLSchoenauerMRigorous hitting times for binary mutationsEvolut. Comput.1999717320310.1162/evco.1999.7.2.173 Doerr, B., Le, H.P., Makhmara, R., Nguyen, T.D.: Fast genetic algorithms. In: Proceedings of GECCO ’17, pp. 777–784. ACM (2017c) NeumannFWegenerIRandomized local search, evolutionary algorithms, and the minimum spanning tree problemTheor. Comput. Sci.20073783240232604910.1016/j.tcs.2006.11.0021117.68090 Doerr, B., Gießen, C., Witt, C., Yang, J.: 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}) evolutionary algorithm with self-adjusting mutation rate. In: Proceedings of GECCO ’17, pp. 1351–1358. ACM (2017b) Mühlenbein, H.: How genetic algorithms really work: Mutation and hillclimbing. In: Proceedings of PPSN ’92, pp. 15–26. Elsevier (1992) Lissovoi, A., Oliveto, P.S., Warwicker, J.A.: On the runtime analysis of generalised selection hyper-heuristics for pseudo-Boolean optimisation. In: Proceedings of GECCO ’17, pp. 849–856. ACM (2017) DoerrBJohannsenDWinzenCMultiplicative drift analysisAlgorithmica201264673697298947010.1007/s00453-012-9622-x1264.68220 JansenTAnalyzing Evolutionary Algorithms—The Computer Science Pe 502_CR43 502_CR44 502_CR41 B Doerr (502_CR11) 2011 502_CR42 H-K Hwang (502_CR35) 2018; 26 H Robbins (502_CR52) 1955; 62 502_CR47 502_CR45 J Garnier (502_CR32) 1999; 7 F Neumann (502_CR48) 2007; 378 502_CR9 502_CR8 502_CR7 R Kaas (502_CR40) 1980; 34 502_CR6 502_CR5 B Doerr (502_CR15) 2015; 561 502_CR4 502_CR3 502_CR2 502_CR1 F Neumann (502_CR49) 2010 AE Eiben (502_CR31) 1999; 3 502_CR39 502_CR34 B Doerr (502_CR13) 2018; 139 B Mitavskiy (502_CR46) 2009; 2 B Doerr (502_CR18) 2012; 64 502_CR21 502_CR22 502_CR20 502_CR29 S Droste (502_CR30) 2002; 276 502_CR27 502_CR28 502_CR25 502_CR26 502_CR23 D Sudholt (502_CR53) 2013; 17 502_CR24 T Jansen (502_CR36) 2013 T Jansen (502_CR38) 2005; 13 502_CR54 502_CR55 M Dietzfelbinger (502_CR10) 2010; 19 502_CR50 502_CR51 502_CR16 502_CR17 502_CR14 B Doerr (502_CR19) 2015; 567 502_CR12 502_CR56 C Gießen (502_CR33) 2017; 78 T Jansen (502_CR37) 2006; 4 |
| References_xml | – reference: Doerr, B., Fouz, M., Witt, C.: Sharp bounds by probability-generating functions and variable drift. In: Proceedings of GECCO ’11, pp. 2083–2090. ACM (2011) – reference: MitavskiyBRoweJECanningsCTheoretical analysis of local search strategies to optimize network communication subject to preserving the total number of linksInt. J. Intell. Comput. Cybern.20092243284275150210.1108/175637809109598931175.90210 – reference: Oliveto, P.S., Lehre, P.K., Neumann, F.: Theoretical analysis of rank-based mutation-combining exploration and exploitation. In: Proceedings of CEC ’09, pp. 1455–1462. IEEE (2009) – reference: Doerr, B., Doerr, C., Kötzing, T.: Provably optimal self-adjusting step sizes for multi-valued decision variables. In: Proceedings of PPSN ’16, pp. 782–791. Springer (2016b) – reference: Giel, O., Wegener, I.: Evolutionary algorithms and the maximum matching problem. In: Proceedings of STACS ’03, pp. 415–426. Springer (2003) – reference: Kötzing, T., Lissovoi, A., Witt, C.: (1+1) EA on generalized dynamic OneMax. In: Proceedings of FOGA ’15, pp. 40–51. ACM (2015) – reference: DoerrBJohannsenDWinzenCMultiplicative drift analysisAlgorithmica201264673697298947010.1007/s00453-012-9622-x1264.68220 – reference: EibenAEHinterdingRMichalewiczZParameter control in evolutionary algorithmsIEEE Trans. Evolut. Comput.1999312414110.1109/4235.771166 – reference: NeumannFWegenerIRandomized local search, evolutionary algorithms, and the minimum spanning tree problemTheor. Comput. Sci.20073783240232604910.1016/j.tcs.2006.11.0021117.68090 – reference: Cathabard, S., Lehre, P.K., Yao, X.: Non-uniform mutation rates for problems with unknown solution lengths. In: Proceedings of FOGA ’11, pp. 173–180. ACM (2011) – reference: Böttcher, S., Doerr, B., Neumann, F.: Optimal fixed and adaptive mutation rates for the LeadingOnes problem. In: Proceedings of PPSN ’10, pp. 1–10. Springer (2010) – reference: Badkobeh, G., Lehre, P.K., Sudholt, D.: Unbiased black-box complexity of parallel search. In: Proceedings of PPSN ’14, pp. 892–901. Springer (2014) – reference: DoerrBAugerADoerrBAnalyzing randomized search heuristics: tools from probability theoryTheory of Randomized Search Heuristics2011SingaporeWorld Scientific Publishing120 – reference: Doerr, B., Doerr, C., Kötzing, T.: The right mutation strength for multi-valued decision variables. In: Proceedings of GECCO ’16, pp. 1115–1122. ACM (2016a) – reference: KaasRBuhrmanJMMean, median and mode in binomial distributionsStat. Neerl.198034131857600510.1111/j.1467-9574.1980.tb00681.x0444.62021 – reference: Johannsen, D.: Random combinatorial structures and randomized search heuristics. Ph.D. thesis, Saarland University (2010) – reference: Doerr, B., Doerr, C., Kötzing, T.: Solving problems with unknown solution length at (almost) no extra cost. In: Proceedings of GECCO ’15, pp. 831–838. ACM (2015b) – reference: DoerrBKünnemannMOptimizing linear functions with 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}) evolutionary algorithm—different asymptotic runtimes for different instancesTheor. Comput. Sci.2015561323328218110.1016/j.tcs.2014.03.0151303.68120 – reference: Doerr, B., Doerr, C., Yang, J.: k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}-bit mutation with self-adjusting k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document} outperforms standard bit mutation. In: Proceedings of PPSN ’16, pp. 824–834. Springer (2016d) – reference: Lehre, P.K., Özcan, E.: A runtime analysis of simple hyper-heuristics: to mix or not to mix operators. In: Proceedings of FOGA ’13, pp. 97–104. ACM (2013) – reference: DoerrBAn elementary analysis of the probability that a binomial random variable exceeds its expectationStat. Probab. Lett.20181396774380218510.1016/j.spl.2018.03.0161392.60022 – reference: Wegener, I.: Simulated annealing beats Metropolis in combinatorial optimization. In: Proceedings of ICALP ’05, pp. 589–601. Springer (2005) – reference: Doerr, B., Le, H.P., Makhmara, R., Nguyen, T.D.: Fast genetic algorithms. In: Proceedings of GECCO ’17, pp. 777–784. ACM (2017c) – reference: RobbinsHA remark on Stirling’s formulaAm. Math. Mon.1955622629693280068.05404 – reference: Doerr, B., Witt, C., Yang, J.: Runtime analysis for self-adaptive mutation rates. In: Proc. GECCO ’18, pp. 1475–1482. ACM (2018b) – reference: Lissovoi, A., Oliveto, P.S., Warwicker, J.A.: On the runtime analysis of generalised selection hyper-heuristics for pseudo-Boolean optimisation. In: Proceedings of GECCO ’17, pp. 849–856. ACM (2017) – reference: Doerr, B., Doerr, C., Kötzing, T.: Unknown solution length problems with no asymptotically optimal run time. In: Proceedings of GECCO ’17, pp. 1367–1374. ACM (2017a) – reference: Alanazi, F., Lehre, P.K.: Runtime analysis of selection hyper-heuristics with classical learning mechanisms. In: Proceedings of CEC ’14, pp. 2515–2523. IEEE (2014) – reference: GarnierJKallelLSchoenauerMRigorous hitting times for binary mutationsEvolut. Comput.1999717320310.1162/evco.1999.7.2.173 – reference: Antipov, D., Doerr, B., Fang, J., Hetet, T.: Runtime analysis for the (μ+λ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\mu +\lambda )$$\end{document} EA optimizing OneMax. In: Proceedings of GECCO ’18, pp. 1459–1466. ACM (2018) – reference: Doerr, B., Gießen, C., Witt, C., Yang, J.: 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}) evolutionary algorithm with self-adjusting mutation rate. In: Proceedings of GECCO ’17, pp. 1351–1358. ACM (2017b) – reference: DrosteSJansenTWegenerIOn the analysis of the (1+1) evolutionary algorithmTheor. Comput. Sci.20022765181189634710.1016/S0304-3975(01)00182-71002.68037 – reference: Buzdalov, M., Doerr, B.: Runtime analysis of 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}$$(1+(\lambda ,\lambda ))$$\end{document} genetic algorithm on random satisfiable 3-CNF formulas. In: Proceedings of GECCO ’17, pp. 1343–1350. ACM (2017) – reference: NeumannFWittCBioinspired Computation in Combinatorial Optimization—Algorithms and Their Computational Complexity2010BerlinSpringer1223.68002 – reference: JansenTAnalyzing Evolutionary Algorithms—The Computer Science Perspective2013BerlinSpringer10.1007/978-3-642-17339-41282.68008 – reference: Zarges, C.: Rigorous runtime analysis of inversely fitness proportional mutation rates. In: Proceedings of PPSN ’08, pp. 112–122. Springer (2008) – reference: Cervantes, J., Stephens, C.R.: Rank based variation operators for genetic algorithms. In: Proceedings of GECCO ’08, pp. 905–912. ACM (2008) – reference: DoerrBDoerrCEbelFFrom black-box complexity to designing new genetic algorithmsTheor. Comput. Sci.201556787104329562610.1016/j.tcs.2014.11.0281314.68290 – reference: Lehre, P.K., Witt, C.: Concentrated hitting times of randomized search heuristics with variable drift. In: Proceedings of ISAAC ’14, pp. 686–697. Springer (2014) – reference: Auger, A., Doerr, B. (eds.): Theory of Randomized Search Heuristics. World Scientific Publishing, Singapore (2011) – reference: Doerr, B., Fouz, M., Witt, C.: Quasirandom evolutionary algorithms. In: Proceedings of GECCO ’10, pp. 1457–1464. ACM (2010) – reference: HwangH-KPanholzerARolinNTsaiT-HChenW-MProbabilistic analysis of the (1+1)-evolutionary algorithmEvolut. Comput.20182629934510.1162/evco_a_00212 – reference: Qian, C., Tang, K., Zhou, Z-H.: Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization. In: Proceedings of PPSN ’16, pp. 835–846. Springer (2016) – reference: DietzfelbingerMRoweJEWegenerIWoelfelPTight bounds for blind search on the integers and the realsComb. Probab. Comput.201019711728272607610.1017/S09635483099905991261.68069 – reference: Doerr, B.: Optimal parameter settings 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}$$(1+(\lambda , \lambda ))$$\end{document} genetic algorithm. In: Proceedings of GECCO ’16, pp. 1107–1114. ACM (2016) – reference: Doerr, B., Doerr, C., Yang, J.: Optimal parameter choices via precise black-box analysis. In: Proceedings of GECCO ’16, pp. 1123–1130. ACM (2016c) – reference: JansenTDe JongKAWegenerIOn the choice of the offspring population size in evolutionary algorithmsEvolut. Comput.20051341344010.1162/106365605774666921 – reference: Doerr, B., Lissovoi, A., Oliveto, P.S., Warwicker, J.A.: On the runtime analysis of selection hyper-heuristics with adaptive learning periods. In: Proceedings of GECCO ’18, pp. 1015–1022. ACM (2018a) – reference: Lässig, J., Sudholt, D.: Adaptive population models for offspring populations and parallel evolutionary algorithms. In: Proceedings of FOGA ’11, pp. 181–192. ACM (2011) – reference: JansenTWegenerIOn the analysis of a dynamic evolutionary algorithmJ. Discrete Algorithms20064181199221174510.1016/j.jda.2005.01.0021128.68118 – reference: Dang, D-C., Lehre, P.K.: Self-adaptation of mutation rates in non-elitist populations. In: Proceedings of PPSN ’16, pp. 803–813. Springer (2016) – reference: SudholtDA new method for lower bounds on the running time of evolutionary algorithmsIEEE Trans. Evolut. Comput.20131741843510.1109/TEVC.2012.2202241 – reference: Mühlenbein, H.: How genetic algorithms really work: Mutation and hillclimbing. In: Proceedings of PPSN ’92, pp. 15–26. Elsevier (1992) – reference: Zarges, C.: On the utility of the population size for inversely fitness proportional mutation rates. In: Proceedings of FOGA ’09, pp. 39–46. ACM (2009) – reference: GießenCWittCThe interplay of population size and mutation probability in 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 OneMaxAlgorithmica201778587609364191210.1007/s00453-016-0214-z1366.68262 – reference: Doerr, B., Doerr, C.: Optimal parameter choices through self-adjustment: applying the 1/5-th rule in discrete settings. In: Proceedings of GECCO ’15, pp. 1335–1342. ACM (2015) – ident: 502_CR34 doi: 10.1007/3-540-36494-3_37 – volume: 4 start-page: 181 year: 2006 ident: 502_CR37 publication-title: J. Discrete Algorithms doi: 10.1016/j.jda.2005.01.002 – ident: 502_CR47 – ident: 502_CR22 doi: 10.1007/978-3-319-45823-6_73 – volume: 17 start-page: 418 year: 2013 ident: 502_CR53 publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/TEVC.2012.2202241 – volume: 139 start-page: 67 year: 2018 ident: 502_CR13 publication-title: Stat. Probab. Lett. doi: 10.1016/j.spl.2018.03.016 – ident: 502_CR16 doi: 10.1145/1830483.1830749 – ident: 502_CR20 doi: 10.1145/2739480.2754681 – ident: 502_CR21 doi: 10.1145/2908812.2908891 – ident: 502_CR42 doi: 10.1145/1967654.1967671 – volume: 19 start-page: 711 year: 2010 ident: 502_CR10 publication-title: Comb. Probab. Comput. doi: 10.1017/S0963548309990599 – volume: 561 start-page: 3 year: 2015 ident: 502_CR15 publication-title: Theor. Comput. Sci. doi: 10.1016/j.tcs.2014.03.015 – volume-title: Bioinspired Computation in Combinatorial Optimization—Algorithms and Their Computational Complexity year: 2010 ident: 502_CR49 – volume: 34 start-page: 13 year: 1980 ident: 502_CR40 publication-title: Stat. Neerl. doi: 10.1111/j.1467-9574.1980.tb00681.x – ident: 502_CR3 doi: 10.1142/7438 – volume: 378 start-page: 32 year: 2007 ident: 502_CR48 publication-title: Theor. Comput. Sci. doi: 10.1016/j.tcs.2006.11.002 – ident: 502_CR9 doi: 10.1007/978-3-319-45823-6_75 – ident: 502_CR54 doi: 10.1007/11523468_48 – ident: 502_CR27 doi: 10.1145/3071178.3071301 – ident: 502_CR12 doi: 10.1145/2908812.2908885 – volume: 276 start-page: 51 year: 2002 ident: 502_CR30 publication-title: Theor. Comput. Sci. doi: 10.1016/S0304-3975(01)00182-7 – ident: 502_CR25 doi: 10.1145/3071178.3071233 – ident: 502_CR55 doi: 10.1007/978-3-540-87700-4_12 – volume: 7 start-page: 173 year: 1999 ident: 502_CR32 publication-title: Evolut. Comput. doi: 10.1162/evco.1999.7.2.173 – volume: 2 start-page: 243 year: 2009 ident: 502_CR46 publication-title: Int. J. Intell. Comput. Cybern. doi: 10.1108/17563780910959893 – ident: 502_CR17 doi: 10.1145/2001576.2001856 – ident: 502_CR8 doi: 10.1145/1389095.1389271 – ident: 502_CR50 doi: 10.1109/CEC.2009.4983114 – ident: 502_CR41 doi: 10.1145/2725494.2725502 – start-page: 1 volume-title: Theory of Randomized Search Heuristics year: 2011 ident: 502_CR11 – volume: 13 start-page: 413 year: 2005 ident: 502_CR38 publication-title: Evolut. Comput. doi: 10.1162/106365605774666921 – ident: 502_CR45 doi: 10.1145/3071178.3071288 – volume: 26 start-page: 299 year: 2018 ident: 502_CR35 publication-title: Evolut. Comput. doi: 10.1162/evco_a_00212 – ident: 502_CR1 doi: 10.1109/CEC.2014.6900602 – ident: 502_CR2 doi: 10.1145/3205455.3205627 – ident: 502_CR7 doi: 10.1145/1967654.1967670 – ident: 502_CR28 doi: 10.1145/3205455.3205611 – ident: 502_CR14 doi: 10.1145/2739480.2754684 – ident: 502_CR23 doi: 10.1145/2908812.2908950 – volume: 3 start-page: 124 year: 1999 ident: 502_CR31 publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/4235.771166 – ident: 502_CR6 doi: 10.1145/3071178.3071297 – ident: 502_CR4 doi: 10.1007/978-3-319-10762-2_88 – ident: 502_CR39 – volume: 567 start-page: 87 year: 2015 ident: 502_CR19 publication-title: Theor. Comput. Sci. doi: 10.1016/j.tcs.2014.11.028 – volume: 62 start-page: 26 year: 1955 ident: 502_CR52 publication-title: Am. Math. Mon. – ident: 502_CR5 doi: 10.1007/978-3-642-15844-5_1 – volume-title: Analyzing Evolutionary Algorithms—The Computer Science Perspective year: 2013 ident: 502_CR36 doi: 10.1007/978-3-642-17339-4 – ident: 502_CR56 doi: 10.1145/1527125.1527132 – ident: 502_CR24 doi: 10.1007/978-3-319-45823-6_77 – volume: 78 start-page: 587 year: 2017 ident: 502_CR33 publication-title: Algorithmica doi: 10.1007/s00453-016-0214-z – ident: 502_CR51 doi: 10.1007/978-3-319-45823-6_78 – ident: 502_CR43 doi: 10.1145/2460239.2460249 – ident: 502_CR26 doi: 10.1145/3071178.3071279 – volume: 64 start-page: 673 year: 2012 ident: 502_CR18 publication-title: Algorithmica doi: 10.1007/s00453-012-9622-x – ident: 502_CR29 doi: 10.1145/3205455.3205569 – ident: 502_CR44 doi: 10.1007/978-3-319-13075-0_54 |
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| SubjectTerms | Algorithm Analysis and Problem Complexity Algorithms Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Evolutionary algorithms Fitness Genetic algorithms Mathematics of Computing Mutation Optimization Parameters Special Issue on Theory of Genetic and Evolutionary Computation Theory of Computation Yeast |
| Title | The (1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate |
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