Skip to main content
Springer Nature Link
Log in

Evolutionary design of Evolutionary Algorithms

  • Original Paper
  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

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

Similar content being viewed by others

Explore related subjects

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

References

  1. J.H. Holland, Adaptation in Natural Artificial Systems (University of Michigan Press, Ann Arbor, 1975)

    Google Scholar 

  2. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley, 1989)

  3. D.H. Wolpert, W.G. Macready, No Free Lunch Theorems for Search. Technical Report SFI-TR-95-02-010 (Santa Fe Institute, 1995)

  4. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  5. G. Syswerda, in A Study of Reproduction in Generational and Steady State Genetic Algorithms, ed. by G.J.E. Rawlins. Proceedings of Foundations of Genetic Algorithms Conference (Morgan Kaufmann, 1991), pp. 94–101

  6. B.J. Ross, Searching for Search Algorithms: Experiments in Meta-Search. Technical Report CS-02-23 (Department of Computer Science, Brock University, 2002)

  7. P.J. Angeline (1995) in Adaptive and Self-adaptive Evolutionary Computations, ed. by M. Palaniswami, Y. Attikiouzel, R. Marks, D. Fogel, T. Fukuda. Computational Intelligence: A Dynamic Systems Perspective (IEEE Press, Piscataway, NJ, 1995), pp. 152–163

  8. P.J., Angeline, in Two Self-Adaptive Crossover Operators for Genetic Programming, ed. by P.J. Angeline, K.E. Kinnear Jr. Advances in Genetic Programming 2 (MIT Press, Cambridge, MA, USA, 1996), pp. 89–110

  9. B. Edmonds, Meta-genetic programming: co-evolving the operators of variation. Elektrik 9(1), 13–29 (2001)

    Google Scholar 

  10. C.R. Stephens, I.G. Olmedo, J.M. Vargas, H. Waelbroeck Self-adaptation in evolving systems. Artificial Life 4(2), 183–201 (1998)

    Article  Google Scholar 

  11. A. Teller, in Evolving Programmers: The Co-evolution of Intelligent Recombination Operators, ed. by P. Angeline, K. Kinnear. Advances in Genetic Programming II (MIT Press, Cambridge, MA, 1996), pp. 45–68

  12. L. Spector, A.J. Robinson, Genetic programming and autoconstructive evolution with the push programming language. Genet. Program. Evol. Mach. 3(1), 7–40 (2002)

    Article  MATH  Google Scholar 

  13. W. Kantschik, P. Dittrich, M. Brameier, W. Banzhaf, in Meta-Evolution in Graph Genetic Programming, ed. by R. Poli, P. Nordin, W.B. Langdon, T.C. Fogarty. Genetic Programming, Proceedings of EuroGP’99. Volume 1598 of LNCS (Springer-Verlag, 1999), pp. 15–28

  14. M. Oltean, in Evolving Evolutionary Algorithms for Function Optimization, ed. by Ken Chen et al. The 7th Joint Conference on Information Sciences, vol. 1 (Association for Intelligent Machinery, 2003), pp. 295–298

  15. M. Oltean, Evolving evolutionary algorithms using linear genetic programming. Evol. Comput. 13(3), 387–410 (2005)

    Article  Google Scholar 

  16. M. Brameier,W. Banzhaf, A comparison of linear genetic programming and neural networks in medical data mining. IEEE-EC 5(1), 17–26 (2001)

    Google Scholar 

  17. P. Nordin, A compiling genetic programming system that directly manipulates the machine code, ed. by K.E. Kinnear Jr. Advances in Genetic Programming (MIT Press, 1994), pp. 311–332

  18. M. Oltean, C. Grosan, in Evolving Evolutionary Algorithms Using Multi Expression Programming, ed. by W. Banzhaf, T. Christaller, P. Dittrich, J.T. Kim, J. Ziegler. Proceedings of European Conference on Artificial Life: Advances in Artificial Life. Volume 2801 of Lecture Notes in Artificial Intelligence (Springer, 2003), pp. 651–658

  19. M. Oltean, Evolving evolutionary algorithms with patterns. Soft Comput. 11(6), 503–518 (2007)

    Article  Google Scholar 

  20. L. Dioşan, M. Oltean, in Evolving evolutionary algorithms using evolutionary algorithms, ed. by Peter A.N. Bosman. GECCO 2007, Late Breaking Paper (2007, pp. 2442–2449)

  21. B. Wolfgang, Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications (Morgan Kaufmann, 1998)

  22. G. Syswerda, in Uniform Crossover in Genetic Algorithms. Proceedings of the Third International Conference on Genetic Algorithms (San Francisco, CA, USA, Morgan Kaufmann, 1989), pp. 2–9

  23. M. Oltean, C. Grosan, A comparison of several linear genetic programming techniques. Complex Syst. 14(4), 285–313 (2004)

    Google Scholar 

  24. J.F. Miller, P. Thomson, in Cartesian genetic programming, ed. by R. Poli, W. Banzhaf, W.B. Langdon, J.F. Miller, P. Nordin, T.C. Fogarty. Proceedings of European Conference on Genetic Programming (EuroGP). Volume 1802 of Lecture Notes in Computer Science (Springer-Verlag, 2000), pp. 121–132

  25. J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs (MIT Press, 1994)

  26. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (Springer, 1992)

  27. L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence through Simulated Evolution (John Wiley & Sons, New York, 1966)

    MATH  Google Scholar 

  28. X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster. IEEE-EC 3(2), 82 (1999)

    Google Scholar 

  29. P. Merz, B. Freisleben, in Genetic local search for the TSP: New results. Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (IEEE Press, 1997), pp. 159–164

  30. N. Krasnogor, Studies on the Theory and Design Space of Memetic Algorithms. PhD thesis, University of the West of England, Bristol, 2002

  31. J.D. Lohn, D.S. Linden, G.S. Hornby, W.F. Kraus, A. Rodriguez-Arroyo, S.E. Seufert, in Evolutionary Design of an x-band Antenna for Nasa’s Space Technology 5 Mission. Evolvable Hardware, 2003. Proceedings. NASA/DoD Conference (2003), pp. 155–163

  32. J.F. Miller, D. Job, V.K. Vassilev, Principles in the evolutionary design of digital circuits—part I. Genet. Program. Evol. Mach. 1(1–2), 7–35 (2000)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by grant IDEI-543 from CNCSIS.

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Kogalniceanu 1, Cluj-Napoca, 400084, Romania

    Laura Dioşan & Mihai Oltean

Authors
  1. Laura Dioşan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mihai Oltean
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Laura Dioşan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dioşan, L., Oltean, M. Evolutionary design of Evolutionary Algorithms. Genet Program Evolvable Mach 10, 263–306 (2009). https://doi.org/10.1007/s10710-009-9081-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-009-9081-6

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy