Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Intelligence

Fuzzy Days 1997: Computational Intelligence Theory and Applications pp 337–342Cite as

A retrospective view and outlook on evolutionary algorithms

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1226))

Abstract

Evolutionary algorithms have been studied for over 35 years. This paper provides a brief summary of the similarities and differences of various methods in evolutionary computation, as well as some ideas for future avenues of research.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • P.J. Angeline (1996) “The effects of noise on self-adaptive evolutionary optimization,” Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L.J. Fogel, P.J. Angeline, and T. Bäck (eds.), MIT Press, Cambridge, MA, pp. 433–439.

    Google Scholar 

  • P.J. Angeline, D.B. Fogel, and L.J. Fogel (1996) “A comparison of self-adaptation methods for finite state machines in dynamic environments,” Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L.J. Fogel, P.J. Angeline, and T. Bäck (eds.), MIT Press, Cambridge, MA, pp. 441–449.

    Google Scholar 

  • P.J. Angeline, R.G. Reynolds, J.R. McDonnell, and R.C. Eberhart (eds.) (1997) Evolutionary Programming VI: Proceedings of the Sixth Annual Conference on Evolutionary Programming, Springer, Berlin.

    Google Scholar 

  • T. Bäck (1996) Evolutionary Algorithms in Theory and Practice, Oxford, NY.

    Google Scholar 

  • T. Bäck and H.-P. Schwefel (1993) “An overview of evolutionary algorithms for parameter optimization,” Evol. Comp., Vol. 1:1, pp. 1–24.

    Google Scholar 

  • H.-G. Beyer (1995) “Toward a theory of evolution strategies: on the benefit of sex — the (μ/μ,λ)-theory,” Evol. Comp., Vol. 3:1, pp. 81–111.

    Google Scholar 

  • K. Chellapilla and D.B. Fogel (1997) “Exploring Self-Adaptive Methods to Improve the Efficiency of Generating Approximate Solutions to Traveling Salesman Problems Using Evolutionary Programming,” Evolutionary Programming VI: Proceedings of the Sixth Annual Conference on Evolutionary Programming, P.J. Angeline, R.G. Reynolds, J.R. McDonnell, and R.C. Eberhart (eds.), Springer, Berlin, in press.

    Google Scholar 

  • D.B. Fogel (1993a) “On the philosophical differences between evolutionary algorithms and genetic algorithms,” Proceedings of the Second Annual Conference on Evolutionary Programming, D.B. Fogel and W. Atmar (eds.), Evolutionary Programming Society, La Jolla, CA, pp. 23–29.

    Google Scholar 

  • D.B. Fogel (1993b) “Applying evolutionary programming to selected traveling salesman problems,” Cybernetics and Systems, Vol. 24, pp. 27–36.

    Google Scholar 

  • D.B. Fogel (1994) “Applying evolutionary programming to selected control problems,” Comp. Math. Applic., Vol 27:11, pp. 89–104.

    Google Scholar 

  • D.B. Fogel (1995) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, NY.

    Google Scholar 

  • D.B. Fogel and J.W. Atmar (1990) “Comparing genetic operators with Gaussian mutations in simulated evolutionary processing using linear systems,” Biological Cybernetics, Vol. 63, pp. 111–114.

    Google Scholar 

  • D.B. Fogel, L.J. Fogel and J.W. Atmar (1991) “Meta-evolutionary programming,” Proc. of the Asilomar Conf. on Signals, Systems and Computers, R.R. Chen (ed.), Maple Press, San Jose, CA, pp. 540–545.

    Google Scholar 

  • D.B. Fogel and A. Ghozeil (1997) “A note on representations and operators,” IEEE Trans. Evolutionary Computation, Vol. 1:2, in press.

    Google Scholar 

  • D.B. Fogel and L.C. Stayton (1994) “On the effectiveness of crossover in simulated evolutionary optimization,” BioSystems, Vol 32:3, pp. 171–182.

    Google Scholar 

  • L.J. Fogel (1962) “Autonomous automata,” Industrial Research, Vol. 4, pp. 14–19.

    Google Scholar 

  • L.J. Fogel, A.J. Owens and M.J. Walsh (1966) Artificial Intelligence through Simulated Evolution, John Wiley, NY.

    Google Scholar 

  • L.J. Fogel, P.J. Angeline, and T. Bäck (eds.) (1996) Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, MIT Press, Cambridge, MA.

    Google Scholar 

  • D.K. Gehlhaar and D.B. Fogel (1996) “Tuning evolutionary programming for conformationally flexible molecular docking,” Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, MIT Press, Cambridge, MA, pp. 419–429.

    Google Scholar 

  • D.E. Goldberg (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.

    Google Scholar 

  • D.E. Goldberg (1994) “Genetic and evolutionary algorithms come of age,” Communications of the ACM, Vol. 37, pp. 113–119.

    Google Scholar 

  • J. H. Holland (1975) Adaptation in Natural and Artificial Systems, Univ. Mich. Press, Ann Arbor.

    Google Scholar 

  • P. Jog, J.Y. Suh, and D. Van Gucht (1989) “The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem,” Proceedings of the Third Intern. Conf. on Genetic Algorithms, J.D. Schaffer (ed.), Morgan Kaufmann, San Mateo, CA, pp. 110–115.

    Google Scholar 

  • H. Mühlenbein (1992) “Evolution in time and space — the parallel genetic algorithm,” Foundations of Genetic Algorithms, G.J.E. Rawlins (ed.), Morgan Kaufmann, San Mateo, CA, pp. 316–337.

    Google Scholar 

  • I. Rechenberg (1965) “Cybernetic solution path of an experimental problem,” Royal Aircraft Establishment, Library Translation No. 1122, August.

    Google Scholar 

  • J. Reed, R. Toombs, and N.A. Barricelli (1967) “Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing,” J. Theoret. Biol., Vol. 17, pp. 319–342.

    Google Scholar 

  • M.M. Rizki, L.A. Tamburino, and M.A. Zmuda (1993) “Evolving multi-resolution feature detectors,” Proceedings of the Second Ann. Conf. on Evolutionary Programming, D.B. Fogel and W. Atmar (eds.), Evolutionary Programming Society, La Jolla, CA, pp. 108–118.

    Google Scholar 

  • N.N. Schraudolph and R.K. Belew (1992) “Dynamic parameter encoding for genetic algorithms,” Machine Learning, Vol. 9:1, pp. 9–22.

    Google Scholar 

  • H.-P. Schwefel (1981) Numerical Optimization of Computer Models, John Wiley, Chichester, U.K.

    Google Scholar 

  • H.-P. Schwefel (1995) Evolution and Optimum Seeking, John Wiley, NY.

    Google Scholar 

  • H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (eds.) (1996) Parallel Problem Solving from Nature 4, Springer, Berlin.

    Google Scholar 

  • D. Wolpert and W.G. Macready (1997) “No free lunch theorems for optimization,” IEEE Trans. Evolutionary Computation, Vol. 1:1, in press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Natural Selection, Inc., 3333 North Torrey Pines Ct., Suite 200, 92037, La Jolla, CA

    Lawrence J. Fogel

Authors
  1. Lawrence J. Fogel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Bernd Reusch

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fogel, L.J. (1997). A retrospective view and outlook on evolutionary algorithms. In: Reusch, B. (eds) Computational Intelligence Theory and Applications. Fuzzy Days 1997. Lecture Notes in Computer Science, vol 1226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62868-1_127

Download citation

  • DOI: https://doi.org/10.1007/3-540-62868-1_127

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62868-2

  • Online ISBN: 978-3-540-69031-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation