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2017 | OriginalPaper | Chapter

Population Control in Evolutionary Algorithms: Review and Comparison

Authors : Yuyang Guan, Ling Yang, Weiguo Sheng

Published in: Bio-inspired Computing: Theories and Applications

Publisher: Springer Singapore

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Abstract

Population size in evolutionary algorithms (EAs) is critical for their performance. In this paper, we first give a comprehensive review of existing population control methods. Then, a few representative methods are selected and empirically compared on a range of well-known benchmark functions to show their pros and cons.

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Literature
1.
2.
Costa, J.C., Tavares, R., Rosa, A.: An experimental study on dynamic random variation of population size. In: 1999 IEEE International Conference on Systems, Man, and Cybernetics, IEEE SMC 1999 Conference Proceedings, vol. 1, pp. 607–612. IEEE (1999)
3.
Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)CrossRef
4.
5.
6.
Romero, G., Mora, A.M., Fernandes, C.: Studying the effect of population size in distributed evolutionary algorithms on heterogeneous clusters. Appl. Soft. Comput. 38(C), 530–547 (2016)
7.
Goldberg, D.E.: Sizing populations for serial and parallel genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 70–79 (1989)
8.
Schaffer, J.: A study of control parameters affecting online performance of genetic algorithms for function optimization, San Meteo, California (1989)
9.
Smith, R.E., Smuda, E.: Adaptively resizing populations: algorithm, analysis, and first results. Complex Syst. 9, 47–72 (1995)
10.
Weise, T., Wu, Y., Chiong, R.J.: Global versus local search: the impact of population sizes on evolutionary algorithm performance. J. Global. Optim. 66(3), 511–534 (2016)CrossRefMATHMathSciNet
11.
Arabas, J., Michalewicz, Z., Mulawka, J.: GAVaPS-a genetic algorithm with varying population size. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, pp. 73–78. IEEE (1994)
12.
Fernández, F., Tomassini, M., Vanneschi, L.: An empirical study of multipopulation genetic programming. Genetic Program. Evol. Mach. 4(1), 21–51 (2003)CrossRefMATH
13.
Brest, J., Maučec, M.S.: Population size reduction for the differential evolution algorithm. Appl. Intell. 29(3), 228–247 (2008)CrossRef
14.
Ahrari, A., Shariat-Panahi, M.: An improved evolution strategy with adaptive population size. Optimization 64(12), 2567–2586 (2015)CrossRefMATHMathSciNet
15.
Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015)CrossRef
16.
Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017)CrossRef
17.
Holdener, E.A.: The art of parameterless evolutionary algorithms. Ph.D. thesis, Missouri University of Science and Technology (2008)
18.
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Urbana 51, 61801 (1991)
19.
Reeves, C.R.: Using genetic algorithms with small populations. In: ICGA, vol. 590, p. 92 (1993)
20.
Goldberg, D.E., Sastry, K., Latoza, T.: On the supply of building blocks. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 336–342. Morgan Kaufmann Publishers Inc. (2001)
21.
De Jong, K.A.: Analysis of the behavior of a class of genetic adaptive systems (1975)
22.
Harik, G., Cantú-Paz, E., Goldberg, D.E., Miller, B.L.: The Gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evol. Comput. 7(3), 231–253 (1999)CrossRef
23.
Fernandez, F., Vanneschi, L., Tomassini, M.: The effect of plagues in genetic programming: a study of variable-size populations. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 317–326. Springer, Heidelberg (2003). https://​doi.​org/​10.​1007/​3-540-36599-0_​29 CrossRef
24.
Fernandez, F., Tomassini, M., Vanneschi, L.: Saving computational effort in genetic programming by means of plagues. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 3, pp. 2042–2049. IEEE (2003)
25.
26.
Brest, J., Zamuda, A., Fister, I., Maučec, M.S.: Large scale global optimization using self-adaptive differential evolution algorithm. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)
27.
Brest, J., Maučec, M.S.: Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Comput. 15(11), 2157–2174 (2011)CrossRef
28.
Zamuda, A., Brest, J., Mezura-Montes, E.: Structured population size reduction differential evolution with multiple mutation strategies on CEC 2013 real parameter optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1925–1931. IEEE (2013)
29.
Yang, M., Cai, Z., Guan, J., Gong, W.: Differential evolution with improved population reduction. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 143–144. ACM (2011)
30.
Ali, M.Z., Awad, N.H., Suganthan, P.N., Reynolds, R.G.: An adaptive multipopulation differential evolution with dynamic population reduction. IEEE Trans. Cybern. 47(9), 2768–2779 (2017)CrossRef
31.
Iacca, G., Mallipeddi, R., Mininno, E., Neri, F.: Super-fit and population size reduction in compact differential evolution. In: Memetic Computing, pp. 1–8 (2011)
32.
Zamuda, A., Brest, J.: Population reduction differential evolution with multiple mutation strategies in real world industry challenges. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC/SIDE -2012. LNCS, vol. 7269, pp. 154–161. Springer, Heidelberg (2012). https://​doi.​org/​10.​1007/​978-3-642-29353-5_​18 CrossRef
33.
Brest, J., Zamuda, A., Fister, I., Maučec, M.S., et al.: Self-adaptive differential evolution algorithm with a small and varying population size. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)
34.
Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)CrossRef
35.
Koumousis, V.K., Katsaras, C.P.: A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–28 (2006)CrossRef
36.
Koumousis, V., Dimou, C.: The effect of oscillating population size on the performance of genetic algorithms. In: Proceedings of the 4th GRACM Congress on Computational Mechanics (2002)
37.
Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665. IEEE (2014)
38.
Yuan, X., Zhang, B., Wang, P., Liang, J., Yuan, Y., Huang, Y., Lei, X.: Multi-objective optimal power flow based on improved strength pareto evolutionary algorithm. Energy 122, 70–82 (2017)CrossRef
39.
Polakova, R., Tvrdik, J., Bujok, P.: Evaluating the performance of l-shade with competing strategies on CEC 2014 single parameter-operator test suite. In: IEEE Congress on Evolutionary Computation, pp. 1181–1187 (2016)
40.
Viktorin, A., Pluhacek, M., Senkerik, R.: Network based linear population size reduction in shade. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 86–93 (2016)
41.
Guo, S.M., Tsai, S.H., Yang, C.C., Hsu, P.H.: A self-optimization approach for l-shade incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: Evolutionary Computation, pp. 1003–1010 (2015)
42.
Zheng, Y.J., Zhang, B.: A simplified water wave optimization algorithm. In: Evolutionary Computation, pp. 807–813 (2015)
43.
Montiel, O., Castillo, O., Melin, P., Sepúlveda, R.: Intelligent control of dynamic population size for evolutionary algorithms. In: IC-AI, pp. 551–557 (2006)
44.
Wang, H., Rahnamayan, S., Wu, Z.: Adaptive differential evolution with variable population size for solving high-dimensional problems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 2626–2632. IEEE (2011)
45.
Wang, X., Zhao, S., Jin, Y., Zhang, L.: Differential evolution algorithm based on self-adaptive adjustment mechanism. In: 2013 25th Chinese Control and Decision Conference (CCDC), pp. 577–581. IEEE (2013)
46.
Elsayed, S.M., Sarker, R.A.: Differential evolution with automatic population injection scheme for constrained problems. In: 2013 IEEE Symposium on Differential Evolution (SDE), pp. 112–118. IEEE (2013)
47.
Zhang, C., Chen, J., Xin, B., Cai, T., Chen, C.: Differential evolution with adaptive population size combining lifetime and extinction mechanisms. In: 2011 8th Asian Control Conference (ASCC), pp. 1221–1226. IEEE (2011)
48.
Zhao, S., Wang, X., Chen, L., Zhu, W.: A novel self-adaptive differential evolution algorithm with population size adjustment scheme. Arab. J. Sci. Eng. 39(8), 6149–6174 (2014)CrossRef
49.
Schlierkamp-Voosen, D., Muhlenbein, H.: Adaptation of population sizes by competing subpopulations. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 330–335. IEEE (1996)
50.
Smorodkina, E., Tauritz, D.: Greedy population sizing for evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 2181–2187. IEEE (2007)
51.
52.
Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 1, pp. 258–265. Morgan Kaufmann Publishers Inc. (1999)
53.
Zhan, Z.H., Zhang, J.: Co-evolutionary differential evolution with dynamic population size and adaptive migration strategy. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 211–212. ACM (2011)
54.
Fernándes, C., Rosa, A.C., Rosa, A.C.: NiGaVaPS - a outbreeding in genetic algorithms. In: ACM Symposium on Applied Computing, pp. 477–482 (2000)
55.
Fernándes, C., Rosa, A.: Self-regulated population size in evolutionary algorithms. In: International Conference on Parallel Problem Solving from Nature, pp. 920–929 (2006)
56.
Fernándes, C., Rosa, A., Pais, A.R., Norte, T.: A study on non-random mating and varying population size in genetic algorithms using a royal road function. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 60–66 (2001)
57.
Lee, H.S., Lee, J.H., Kim, E.T.: Optimal classifier ensemble design for vehicle detection using GAVaPS. J. Inst. Control Robot. Syst. 16(1), 96–100 (2010)CrossRef
58.
Bäck, T., Eiben, A.E., Van Der Vaart, N.A.L.: An empirical study on gas “Without Parameters”. In: International Conference on Parallel Problem Solving from Nature, pp. 315–324 (2000)
59.
Iorio, A., Li, X.: Parameter control within a co-operative co-evolutionary genetic algorithm. In: Guervós, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fernández-Villacañas, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 247–256. Springer, Heidelberg (2002). https://​doi.​org/​10.​1007/​3-540-45712-7_​24
60.
Vellev, S.: An adaptive genetic algorithm with dynamic population size for optimizing join queries. Adv. Res. Artif. Int. 82, 82–88 (2008)
61.
Cook, J.E., Tauritz, D.R.: An exploration into dynamic population sizing. In: Conference on Genetic and Evolutionary Computation, pp. 807–814 (2010)
62.
Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. In: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, pp. 289–296. International Society for Optics and Photonics (1990)
63.
Coello, C.A., Pulido, G.T.: Multiobjective optimization using a micro-genetic algorithm. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 274–282. Morgan Kaufmann Publishers Inc. (2001)
64.
Xu, Y., Liu, G.: Detection of flaws in composites from scattered elastic-wave field using an improved \(\mu \)GA and a local optimizer. Comput. Methods Appl. Mech. 191(36), 3929–3946 (2002)CrossRefMATH
65.
Ryoo, J., Hajela, P.: Handling variable string lengths in GA-based structural topology optimization. Struct. Multidiscip. Optim. 26(5), 318–325 (2004)CrossRef
66.
Khor, E.F., Tan, K.C., Wang, M.L., Lee, T.H.: Evolutionary algorithm with dynamic population size for multi-objective optimization. In: Conference of the IEEE Industrial Electronics Society, IECON 2000, vol. 4, pp. 2768–2773 (2000)
67.
Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Trans. Evol. Comput. 5(6), 565–588 (2001)CrossRef
68.
Liang, Y., Leung, K.S.: Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl. Soft. Comput. 11(2), 2017–2034 (2011)CrossRef
69.
Yang, M., Cai, Z., Guan, J., Guan, J.: An improved adaptive differential evolution algorithm with population adaptation. In: Conference on Genetic and Evolutionary Computation, pp. 145–152 (2013)
70.
Ding, M., Chen, H., Lin, N., Jing, S., Liu, F., Liang, X., Liu, W.: Dynamic population artificial bee colony algorithm for multi-objective optimal power flow. Saudi. J. Biol. Sci. 24(3), 703–710 (2017)CrossRef
71.
Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1769–1776 (2005)
72.
Shi, E.C., Leung, F.H.F., Law, B.N.F.: Differential evolution with adaptive population size. In: International Conference on Digital Signal Processing, pp. 876–881 (2014)
73.
Smith, R.E., Smuda, E.: Adaptively resizing populations: algorithm, analysis, and first results. Complex Syst. (1993)
74.
75.
Wagner, N., Michalewicz, Z.: Genetic Programming with Efficient Population Control for Financial Time Series Prediction (2001)
76.
Wagner, N., Michalewicz, Z.: Parameter Adaptation for GP Forecasting Applications (2007)
77.
Wagner, N., Michalewicz, Z., Khouja, M., Mcgregor, R.R.: Time series forecasting for dynamic environments: the DyFor genetic program model. IEEE Trans. Evol. Comput. 11(4), 433–452 (2007)CrossRef
78.
Teo, J.: Exploring dynamic self-adaptive populations in differential evolution. Soft Comput. 10(8), 673–686 (2006)CrossRef
79.
Eiben, A.E., Schut, M.C., Wilde, A.R.D.: Is self-adaptation of selection pressure and population size possible?: a case study. In: International Conference on Parallel Problem Solving from Nature, pp. 900–909 (2006)
Metadata
Title
Population Control in Evolutionary Algorithms: Review and Comparison
Authors
Yuyang Guan
Ling Yang
Weiguo Sheng
Copyright Year
2017
Publisher
Springer Singapore
Book
Bio-inspired Computing: Theories and Applications

Print ISBN: 978-981-10-7178-2

Electronic ISBN: 978-981-10-7179-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7179-9_13

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