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Canonical Memetic Algorithms

  • Abhishek GuptaEmail author
  • Yew-Soon Ong
Chapter
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 21)

Abstract

The remarkable flexibility of evolutionary computation (EC) in handling a wide range of problems, encompassing search, optimization, and machine learning, opens up a path to attaining artificial general intelligence. However, it is clear that excessive reliance on purely stochastic evolutionary processes, with no expert guidance or external knowledge incorporation, will often lead to performance characteristics that are simply too slow for practical applications demanding near real-time operations. What is more, the randomness associated with classical evolutionary algorithms (EAs) implies that they may not be the ideal tool of choice for various applications relying on high precision and crisp performance guarantees. These observations provided the impetus for conceptualizing the memetic computation (MC) paradigm, wherein the basic mechanisms of evolution are augmented with domain-knowledge expressed as computationally encoded memes. In this chapter, we introduce what is perhaps the most recognizable algorithmic realization of MC, namely, the canonical memetic algorithm (CMA).

References

  1. 1.
    Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, 1989. Reading: Addison-Wesley.Google Scholar
  2. 2.
    Eiben, A. E., & Rudolph, G. (1999). Theory of evolutionary algorithms: A bird’s eye view. Theoretical Computer Science, 229(1–2), 3–9.MathSciNetCrossRefGoogle Scholar
  3. 3.
    Moscato, P., & Cotta, C. (2010). A modern introduction to memetic algorithms. In Handbook of metaheuristics (pp. 141–183). Boston, MA: Springer.CrossRefGoogle Scholar
  4. 4.
    Nguyen, Q. H., Ong, Y. S., & Lim, M. H. (2009). A probabilistic memetic framework. IEEE Transactions on Evolutionary Computation, 13(3), 604–623.CrossRefGoogle Scholar
  5. 5.
    Hart, W. E. (1994). Adaptive global optimization with local search (Doctoral dissertation, University of California, San Diego, Department of Computer Science & Engineering).Google Scholar
  6. 6.
    Ku, K. W., Mak, M. W., & Siu, W. C. (2000). A study of the Lamarckian evolution of recurrent neural networks. IEEE Transactions on Evolutionary Computation, 4(1), 31–42.CrossRefGoogle Scholar
  7. 7.
    Whitley, D., Gordon, V. S., & Mathias, K. (1994, October). Lamarckian evolution, the Baldwin effect and function optimization. In International Conference on Parallel Problem Solving from Nature (pp. 5–15). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  8. 8.
    Ku, K. W., & Mak, M. W. (1998, September). Empirical analysis of the factors that affect the Baldwin effect. In International Conference on Parallel Problem Solving from Nature (pp. 481–490). Berlin, Heidelberg: Springer.Google Scholar
  9. 9.
    Baldwin, J. M. (1896). A new factor in evolution. The American Naturalist, 30(354), 441–451. CrossRefGoogle Scholar
  10. 10.
    Hinton, G. E., & Nowlan, S. J. (1987). How learning can guide evolution. Complex Systems, 1(3), 495–502.zbMATHGoogle Scholar
  11. 11.
    Pelikan, M., & Goldberg, D. E. (2001, July). Escaping hierarchical traps with competent genetic algorithms. In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (pp. 511–518). Morgan Kaufmann Publishers Inc.Google Scholar
  12. 12.
    Altenberg, L. (1995). The schema theorem and Price’s theorem. In Foundations of genetic algorithms (Vol. 3, pp. 23–49). Elsevier.Google Scholar
  13. 13.
    Pelikan, M., Goldberg, D. E., & Cantú-Paz, E. (1999, July). BOA: The Bayesian optimization algorithm. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation- (Vol. 1, pp. 525–532). Morgan Kaufmann Publishers Inc.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore

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