Article

A review of adaptive population sizing schemes in genetic algorithms

Authors:

Fernando G. Lobo,

Cláudio F. LimaAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

Pages 228 - 234

https://doi.org/10.1145/1102256.1102310

Published: 25 June 2005 Publication History

Abstract

This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various self-adjusting population sizing schemes that have been proposed in the literature. The paper ends with recommendations for those who design and compare adaptive population sizing schemes for genetic algorithms.

References

[1]

J. Arabas, Z. Michalewicz, and J. Mulawka. GAVaPS -- a genetic algorithm with varying population size. In Proc. of the First IEEE Conf. on Evolutionary Computation, pages 73--78. IEEE Press, 1994.]]

[2]

T. Bäck, A. E. Eiben, and N. A. L. van der Vaart. An empirical study on GAs "without parameters". In Parallel Problem Solving from Nature, PPSN VI, volume 1917 of Lecture Notes in Computer Science, pages 315--324. Springer, 2000.]]

Digital Library

[3]

K. A. De Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, 1975.]]

Digital Library

[4]

A. E. Eiben, E. Marchiori, and V. A. Valkó. Evolutionary algorithms with on-the-fly population size adjustment. In Parallel Problem Solving from Nature, PPSN VIII, volume 3242 of Lecture Notes in Computer Science, pages 41--50. Springer, 2004.]]

[5]

D. E. Goldberg. The Design of Innovation - Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell, MA, 2002.]]

Digital Library

[6]

D. E. Goldberg, K. Deb, and J. H. Clark. Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6:333--362, 1992.]]

[7]

D. E. Goldberg and M. Rudnick. Genetic algorithms and the variance of fitness. Complex Systems, 5(3):265--278, 1991.]]

[8]

G. R. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7(3):231--253, 1999.]]

Digital Library

[9]

G. R. Harik and F. G. Lobo. A parameter-less genetic algorithm. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pages 258--267. Morgan Kaufmann, 1999.]]

[10]

R. Hinterding, Z. Michalewicz, and T. C. Peachey. Self-adaptive genetic algorithm for numeric functions. In Parallel Problem Solving from Nature, PPSN IV, pages 420--429. Springer-Verlag, 1996.]]

Digital Library

[11]

J. H. Holland. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2):88--105, 1973.]]

Digital Library

[12]

P. Larrañaga and J. A. Lozano, editors. Estimation of distribution algorithms: a new tool for Evolutionary Computation. Kluwer Academic Publishers, Boston, MA, 2002.]]

[13]

C. F. Lima and F. G. Lobo. Parameter-less optimization with the extended compact genetic algorithm and iterated local search. In GECCO-2004: Proceedings of the Genetic and Evolutionary Computation Conference, Part I, volume 3102 of Lecture Notes in Computer Science, pages 1328--1339. Springer, 2004.]]

[14]

F. G. Lobo. The parameter-less genetic algorithm: Rational and automated parameter selection for simplified genetic algorithm operation. Doctoral dissertation, Universidade Nova de Lisboa, Lisboa, 2000.]]

[15]

F. G. Lobo, D. E. Goldberg, and M. Pelikan. Time complexity of genetic algorithms on exponentially scaled problems. In GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference, pages 151--158. Morgan Kaufmann, 2000.]]

[16]

M. Pelikan, D. E. Goldberg, and E. Cantú-Paz. Bayesian optimization algorithm, population sizing, and time to convergence. In GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference, pages 275--282. Morgan Kaufmann, 2000.]]

[17]

M. Pelikan, D. E. Goldberg, and F. Lobo. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 21(1):5--20, 2002.]]

Digital Library

[18]

M. Pelikan and T.-K. Lin. Parameter-less hierarchical BOA. In GECCO-2004: Proceedings of the Genetic and Evolutionary Computation Conference, Part II, volume 3103 of Lecture Notes in Computer Science, pages 24--35. Springer, 2004.]]

[19]

M. Pelikan and F. G. Lobo. Parameter-less genetic algorithm: a worst-case time and space complexity analysis. IlliGAL Report No. 99014, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, 1999.]]

[20]

M. Pelikan, K. Sastry, and D. E. Goldberg. Scalability of the bayesian optimization algorithm. International Journal of Approximate Reasoning, 31(3):221--258, 2003.]]

[21]

D. Schlierkamp-Voosen and H. Mühlenbein. Strategy adaption by competing subpopulations. In Parallel Problem Solving from Nature, PPSN III, pages 199--208. Springer-Verlag, 1994.]]

Digital Library

[22]

D. Schlierkamp-Voosen and H. Mühlenbein. Adaptation of population sizes by competing subpopulations. In Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pages 330--335, 1996.]]

[23]

R. E. Smith. Adaptively resizing populations: An algorithm and analysis. Technical Report No. 93001, The University of Alabama, Tuscaloosa, AL, 1993.]]

[24]

R. E. Smith and E. Smuda. Adaptively resizing populations: Algorithm, analysis, and first results. Complex Systems, 9:47--72, 1995.]]

[25]

D. Thierens, D. E. Goldberg, and A. Pereira. Domino convergence, drift, and the temporal-salience structure of problems. In Proceedings of 1998 IEEE International Conference on Evolutionary Computation, pages 535--540. IEEE, 1998.]]

[26]

V. A. Valkó. Self-calibrating Evolutionary Algorithms: Adaptive Population Size. Master's thesis, Free University Amsterdam, 2003.]]

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Index Terms

A review of adaptive population sizing schemes in genetic algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
  2. Machine learning
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

June 2005

431 pages

ISBN:9781450378000

DOI:10.1145/1102256

Conference Chair:
Franz Rothlauf
University of Mannheim, Germany

Copyright © 2005 ACM.

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 ACM 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 [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 25 June 2005

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 26, 2005

D.C., Washington

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Tamura K(2024)Evaluation‐Number Constrained Optimization Problem and its Solution StrategyIEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2400019:4(490-506)Online publication date: 22-Jan-2024
https://doi.org/10.1002/tee.24000
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https://doi.org/10.3390/app13106327
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Baldassi C(2022) Recombinator- k -Means: An Evolutionary Algorithm That Exploits k -Means++ for Recombination IEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.314413426:5(991-1003)Online publication date: Oct-2022
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