Skip to main content

Advertisement

Springer Nature Link
Log in

On the class of hybrid adaptive evolutionary algorithms (chavela)

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

There is no doubt that both determining theoretical properties and characterizing the observed behavior of an evolutionary algorithm allow us to understand when to use such an algorithm in solving a class of optimization problems. One of those evolutionary algorithms is the Hybrid Adaptive Evolutionary Algorithm (haea). The general scheme followed by a haea algorithm is to evolve every individual of the population by selecting genetic operators according to a kind of chaotic competition mechanism. This paper proposes and studies, from both theoretical and experimental points of view, the class of hybrid adaptive evolutionary algorithms (called chavela), i.e., the class of evolutionary algorithms that follow such a general scheme. In this way, this paper presents a formal characterization of the chavela class in terms of Markov kernels; establishes convergence properties; proves that (parallel) hill-climbing algorithms belong to the chavela class; develops generational, steady-state, and classic versions; and analyzes the running behavior of chavela on well-known optimization functions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1:3–52. https://doi.org/10.1023/A:1015059928466

    Article  MathSciNet  MATH  Google Scholar 

  • Bowerman BL (1974) Nonstationary markov decision processes and related topics in nonstationary markov chains. Ph.D thesis, University of Iowa, https://lib.dr.iastate.edu/rtd/6327

  • Breiman L (1968) Probability. Addison-Wesley, Cambridge

    MATH  Google Scholar 

  • Cantor G, Gómez J (2010) Maintaining genetic diversity in fine-grained parallel genetic algorithms by combining cellular automata, cambrian explosions and massive extinctions. In: IEEE Congress on Evolutionary Computation

  • Cruz-Salinas AF, Gomez J (2017) Self-adaptation of genetic operators through genetic programming techniques. In: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, GECCO ’17, p 913–920, https://doi.org/10.1145/3071178.3071214,

  • Cubides E, Gomez J (2020) Obtaining basic algebra formulas with genetic programming and functional rewriting. submitted to arXiv

  • De Jong K (1975) An analysis of the Behavior of a class of genetic adaptive systems. Ph.D thesis, University of Michigan

  • Drake JH, Kheiri A, Özcan E, Burke EK (2020) Recent advances in selection hyper-heuristics. Euro J Oper Res 285(2):405–428. https://doi.org/10.1016/j.ejor.2019.07.073

    Article  MathSciNet  MATH  Google Scholar 

  • Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolut Comput 3(2):124–141

    Article  Google Scholar 

  • Forrest S, Mitchell M (1993) Relative building blocks fitness and the building block hypothesis. Foundations of Genetic Algorithms (2)

  • Fristedt BE, Gray LF (1997) A modern approach to probability theory. Springer, Berlin

    Book  Google Scholar 

  • Goldberg D, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530

    MathSciNet  MATH  Google Scholar 

  • Gomez J (2004) Evolution of fuzzy rule based classifiers. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2004

  • Gomez J (2004) Self adaptation of operator rates for multimodal optimization. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), vol 2, pp 1720–1726

  • Gomez J (2004) Self adaptation of operator rates in evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004)

  • Gomez J (2006) An incremental-evolutionary approach for learning deterministic finite automata. In: 2006 IEEE International Conference on Evolutionary Computation, pp 362–369, https://doi.org/10.1109/CEC.2006.1688331

  • Gomez J (2019) Stochastic global optimization algorithms: a systematic formal approach. Inf Sci 472:53–76. https://doi.org/10.1016/j.ins.2018.09.021

    Article  MathSciNet  MATH  Google Scholar 

  • Gomez J, Kozma R (2004) Fuzzy class binarization using coupled map lattices. In: Proceedings of North American Fuzzy Information Processing Society NAFIPS 2004

  • Gomez J, Leon E (2010) A coevolutionary chromosome encoding scheme for high dimensional search spaces. In: IEEE Congress on Evolutionary Computation, https://doi.org/10.1109/CEC.2010.5586359

  • Gomez J, Rivera C (2020) Non-stationary stochastic global optimization algorithms. Submitted-to-Natural- Computation,-available-on- arXiv

  • Gomez J, Garcia A, Silva C (2005) Cofre: a fuzzy rule coevolutionary approach for multiclass classification problems. In: 2005 IEEE Congress on Evolutionary Computation, vol 2, pp 1637–1644, https://doi.org/10.1109/CEC.2005.1554885

  • Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Michigan

    Google Scholar 

  • Karafotias G, Hoogendoorn M, Eiben AE (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol Comput 19(2):167–187

    Article  Google Scholar 

  • Kenkle A (2014) probability theory: a comprehensive course, 2nd edn. Springer, Berlin

    Book  Google Scholar 

  • Kimura M (1983) The neutral theory of molecular evolution. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Leon E (2005) Scalable and adaptive evolutionary clustering for noisy and dynamic data. Ph.D thesis, USA

  • Leon E, Nasraoui O, Gomez J (2006) Ecsago: Evolutionary clustering with self adaptive genetic operators. In: 2006 IEEE International Conference on Evolutionary Computation, pp 1768–1775

  • Leon E, Nasraoui O, Gomez J (2010) Scalable evolutionary clustering algorithm with self adaptive genetic operators. In: IEEE Congress on Evolutionary Computation, https://doi.org/10.1109/CEC.2010.5586467

  • Liberti L (2004) Introduction to global optimization. Monographs of the Sociedad Matematica Peruana

  • Mahfoud SW (1995) A comparison of parallel and sequential niching methods. In: Proceedings of the Sixth International Conference on Genetic Algorithms

  • Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge

    MATH  Google Scholar 

  • Nader-Palacio D, Rodríguez-Cárdenas D, Gomez J (2018) Assessing single-objective performance convergence and time complexity for refactoring detection. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, GECCO ’18, p 1606–1613, https://doi.org/10.1145/3205651.3208294,

  • Nasraoui O, Leon E, Krishnapuram R (2005) Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets, pp 157–188. https://doi.org/10.1007/3-540-32358-9_8

  • Prieto J, Gomez J (2020) Hybrid adaptive evolutionary algorithm for multi-objective optimization. arXiv:2004.13925

  • Prieto J, León E, Garzon MH (2018) Self-adaptive evolutionary algorithm for DNA codeword design. 2018 IEEE Congress on Evolutionary Computation (CEC) pp 1–8

  • Prieto J, Gomez J, Leon E (2019) Multi-objective evolutionary algorithm for DNA codeword design. In: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, GECCO ’19, p 604–611, https://doi.org/10.1145/3321707.3321855

  • Puerta-Jaramillo DL (2016) An evolutionary approach for the optimization of production-distribution network design. Master’s thesis, Universidad Nacional de Colombia-Sede Bogotá, http://bdigital.unal.edu.co/56372/, magister In Computer And Systems Engineering. Línea de Investigación: Evolutionary Algorithms

  • Quiñones TAR (2014) Solución de problemas tipo flow-shop mediante algoritmos evolutivos. Master’s thesis, Universidad Nacional de Colombia, http://bdigital.unal.edu.co/12916/, maestría en Ingeniería Industrial. Línea de investigación Algoritmos Evolutivos

  • Rangaiah GP, Rangaiah GP (2010) Stochastic global optimization techniques and applications in chemical engineering: techniques and applications in chemical engineering. World Scientific Publishing Co., Inc., River Edge

    Book  Google Scholar 

  • Rudolph G (1996) Convergence of evolutionary algorithms in general search spaces. In: Proceedings of the Third IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway (NJ, pp 50–54

  • Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall Press, Upper Saddle River

    MATH  Google Scholar 

  • Schwefel HP (1981) Numerical optimization of computer models. Wiley, New York

    MATH  Google Scholar 

  • Simoes A, Costa E (1999) Transposition: a biologically inspired mechanism to use with genetic algorithms. In: Fourth International Conference on Neural Networks and Genetic Algorithms, pp 612–619

  • Stewart TJ, Ittmann HW (1979) Two-stage optimization in a transportation problem. J. Oper. Res. Soc. 30(10):897–904

    Article  Google Scholar 

  • Whitley D, Rana S, Dzubera J, Mathias KE (1996) Evaluating evolutionary algorithms. Artif Intell 85(1–2):245–276. https://doi.org/10.1016/0004-3702(95)00124-7

    Article  Google Scholar 

  • Zhigljavsky A, Zilinskas A (2010) Stochastic global optimization. Springer Optimization and Its Applications, Springer, http://opac.inria.fr/record=b1132570

Download references

Author information

Authors and Affiliations

  1. Computer Systems Engineering, Engineering School, Universidad Nacional de Colombia, Bogotá, Colombia

    Jonatan Gómez & Elizabeth León

Authors
  1. Jonatan Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth León
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonatan Gómez.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez, J., León, E. On the class of hybrid adaptive evolutionary algorithms (chavela). Nat Comput 20, 377–394 (2021). https://doi.org/10.1007/s11047-021-09843-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-021-09843-5

Keywords

Mathematics Subject Classification