Advertisement

Lamarckian evolution, the Baldwin effect and function optimization

  • Darrell Whitley
  • V. Scott Gordon
  • Keith Mathias
Basic Concepts of Evolutionary Computation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

Abstract

We compare two forms of hybrid genetic search. The first uses Lamarckian evolution, while the second uses a related method where local search is employed to change the fitness of strings, but the acquired improvements do not change the genetic encoding of the individual. The latter search method exploits the Baldwin effect. By modeling a simple genetic algorithm we show that functions exist where simple genetic algorithms without learning as well as Lamarckian evolution converge to the same local optimum, while genetic search utilizing the Baldwin effect converges to the global optimum. We also show that a simple genetic algorithm exploiting the Baldwin effect can sometimes outperform forms of Lamarckian evolution that employ the same local search strategy.

Keywords

Genetic Algorithm Local Search Saddle Point Local Optimum Fitness Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D.H. Ackley and M. Littman, (1991) Interactions between learning and evolution. In, Proc. of the 2nd Conf. on Artificial Life, C.G. Langton, ed., Addison-Wesley, 1991.Google Scholar
  2. [2]
    J.M.Baldwin, (1896) A new factor in evolution. American Naturalist, 30:441–451, 1896.CrossRefGoogle Scholar
  3. [3]
    R.K. Belew, (1989) When both individuals and populations search: Adding simple learning to the Genetic Algorithm. In 3th Intern. Conf. on Genetic Algorithms, D. Schaffer, ed., Morgan Kaufmann.Google Scholar
  4. [4]
    D. Goldberg, (1989) Genetic algorithms and walsh functions: Part II, deception and its analysis. Complex Systems 3:153–171.MathSciNetGoogle Scholar
  5. [5]
    F. Gruau and D. Whitley, (1993) Adding learning to the cellular development of neural networks: evolution and the Baldwin effect. Evolutionary Computation 1(3):213–233.Google Scholar
  6. [6]
    I. Harvey, (1993) The puzzle of the persistent question marks: a case study of genetic drift. In 5th Intern. Conf. on Genetic Algorithms, S. Forrest, ed., Morgan Kaufmann.Google Scholar
  7. [7]
    G.E. Hinton and S.J. Nolan, (1987) How learning can guide evolution. Complex Systems, 1:495–502.Google Scholar
  8. [8]
    M. Vose and G. Liepins, (1991) Punctuated equilibria in genetic search. Complex Systems 5:31–44.Google Scholar
  9. [9]
    D. Whitley, (1991) Fundamental principles of deception. Foundations of Genetic Algorithms. G. Rawlins, ed. Morgan Kaufmann.Google Scholar
  10. [10]
    D. Whitley, R. Das, C. Crabb, (1992) Tracking primary hyperplane competitors during genetic search. Anals of Mathematics and Artificial Intelligence, 6:367–388.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Darrell Whitley
    • 1
  • V. Scott Gordon
    • 1
  • Keith Mathias
    • 1
  1. 1.Computer Science DepartmentColorado State UniversityFort Collins

Personalised recommendations