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Genericity of the Fixed Point Set for the Infinite Population Genetic Algorithm

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Foundations of Genetic Algorithms (FOGA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4436))

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Abstract

The infinite population model for the genetic algorithm, where the iteration of the genetic algorithm corresponds to an iteration of a map G, is a discrete dynamical system. The map G is a composition of a selection operator and a mixing operator, where the latter models the effects of both mutation and crossover. This paper shows that for a typical mixing operator, the fixed point set of G is finite. That is, an arbitrarily small perturbation of the mixing operator will result in a map G with finitely many fixed points. Further, any sufficiently small perturbation of the mixing operator preserves the finiteness of the fixed point set.

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References

  1. Wright, A.H., Vose, M.D.: Stability of vertex fixed points and applications. In: Foundations of Genetic Algorithms 3, Morgan Kaufman Publishers, San Francisco (1995)

    Google Scholar 

  2. Reeves, C.R., Rowe, J.E.: Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory. Kluwer Academic Publishers, Boston, MA (2003)

    MATH  Google Scholar 

  3. Vose, M.D.: The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  4. Wright, A.H., Bidwell, G.: A search for counterexamples to two conjecture on the simple genetic algorithm. In: Foundations of Genetic Algorithms 4, Morgan Kaufman Publishers, San Francisco (1997)

    Google Scholar 

  5. Wright, A.H., Vose, M.D.: Finiteness of the fixed point set for the simple genetic algorithm. Evolutionary Computation 3(4) (1995)

    Google Scholar 

  6. Rowe, J.E., Vose, M.D., Wright, A.H.: Group properties of crossover and mutation. Evolutionary Computation 10(2), 151–184 (2002)

    Article  Google Scholar 

  7. Rowe, J.E.: A normed space of genetic operators with applications to scalability issues. Evolutionary Computation 9(1) (2001)

    Google Scholar 

  8. Abraham, R., Robbin, J.: Transversal Mappings and Flows. W. A. Benjamin, Inc., New York (1967)

    MATH  Google Scholar 

  9. Guillemin, V., Pollack, A.: Differential Topology. Prentice-Hall, Inc., Englewood Cliffs (1974)

    MATH  Google Scholar 

  10. Hirsch, M.: Differential Topology. Springer, Heidelberg (1976)

    MATH  Google Scholar 

  11. J.Jr., P., de Melo, W.: Geometric Theory of Dynamical Systems. Springer, Heidelberg (1982)

    Google Scholar 

  12. Jänich, K.: Vector Analysis. Springer, Heidelberg (2001)

    Google Scholar 

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Christopher R. Stephens Marc Toussaint Darrell Whitley Peter F. Stadler

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© 2007 Springer-Verlag Berlin Heidelberg

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Gedeon, T., Hayes, C., Swanson, R. (2007). Genericity of the Fixed Point Set for the Infinite Population Genetic Algorithm. In: Stephens, C.R., Toussaint, M., Whitley, D., Stadler, P.F. (eds) Foundations of Genetic Algorithms. FOGA 2007. Lecture Notes in Computer Science, vol 4436. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73482-6_6

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  • DOI: https://doi.org/10.1007/978-3-540-73482-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73479-6

  • Online ISBN: 978-3-540-73482-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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