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Modeling building-block interdependency

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

The Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA lest problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various lest problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-1FF) as a canonical example. We present some empirical results of GAs on H-1FF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests.

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References

  1. Altenberg, L, 1995 “The Schema Theorem and Price's Theorem”, FOGA3, editors Whitley & Vose, pp 23–49, Morgan Kauffmann, San Francisco.

    Google Scholar 

  2. Deb, K & Goldberg, DE, 1989, “An investigation of Niche and Species Formation in genetic Function Optimization”, ICGA3, San Mateo, CA: Morgan Kauffman.

    Google Scholar 

  3. Deb, K & Goldberg, DE, 1992, “Sufficient conditions for deceptive and easy binary functions”, (IlliGAL Report No. 91009), University of Illinois, IL.

    Google Scholar 

  4. Forrest, S & Mitchell, M, 1993 “Relative Building-block fitness and the Building-block Hypothesis”, in Foundations of Genetic Algorithms 2, Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  5. Forrest, S & Mitchell, M, 1993b “What makes a problem hard for a Genetic Algorithm? Some anomalous results and their explanation” Machine Learning 13, pp.285–319.

    Article  Google Scholar 

  6. Goldberg, DE, 1989 “Genetic Algorithms in Search, Optimisation and Machine Learning”, Reading Massachusetts, Addison-Wesley.

    Google Scholar 

  7. Goldberg, DE, & Horn, J, 1994 “Genetic Algorithm Difficulty and the Modality of Fitness Landscapes”, in Foundations of Genetic Algorithms 3, Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  8. Goldberg, DE, Deb, K, Kargupta, H, & Harik, G, 1993 “Rapid, Accurate Optimization of Difficult Problems Using Fast Messy GAs”, IlliGAL Report No. 93004, U. of Illinois, IL.

    Google Scholar 

  9. Goldberg, DE, Deb, K, & Korb, B, 1989 “Messy Genetic Algorithms: Motivation, Analysis and first results”, Complex Systems, 3, 493–530.

    MATH  MathSciNet  Google Scholar 

  10. Holland, JH, 1975 “Adaptation in Natural and Artificial Systems”, Ann Arbor, MI: The University of Michigan Press.

    Google Scholar 

  11. Holland, JH, 1993 “Royal Road Functions”, Internet Genetic Algorithms Digest v7n22.

    Google Scholar 

  12. Jones, T, 1995, Evolutionary Algorithms, Fitness Landscapes and Search, PhD dissertation, 95-05-048, University of New Mexico, Albuquerque. pp. 62–65.

    Google Scholar 

  13. Jones, T, & Forrest, S, 1995 “Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms” ICGA 6, Morgan & Kauffman.

    Google Scholar 

  14. Kauffman, SA, 1993 “The Origins of Order”, Oxford University Press.

    Google Scholar 

  15. Michalewicz, Z, 1992, “Genetic Algorithms + Data Structures = Evolution Programs” Springer-Verlag, New York, 1992.

    Google Scholar 

  16. Mitchell, M, Holland, JH, & Forrest, S, 1995 “When will a Genetic Algorithm Outperform Hill-climbing?” to appear in Advances in NIPS 6, Mogan Kaufmann, San Mateo, CA.

    Google Scholar 

  17. Mitchell, M, Forrest, S, & Holland, JH, 1992 “The royal road for genetic algorithms: Fitness landscapes and GA performance”, Procs. of first ECAL, Camb., MA. MIT Press.

    Google Scholar 

  18. Simon, HA, 1969 “The Sciences of the Artificial” Cambridge, MA. MIT Press.

    Google Scholar 

  19. Smith, RE, Forrest, S, & Perelson, A, 1993 “Searching for Diverse, Cooperative Populations with Genetic Algorithms”, Evolutionary Computation 1(2), ppl27–149.

    Google Scholar 

  20. Whitley, D, Mathias, K, Rana, S & Dzubera, J, 1995 “Building Better Test Functions”, ICGA-6, editor Eshelman, pp239–246, Morgan Kauffmann, San Francisco.

    Google Scholar 

  21. Whitley, D, Beveridge, R, Graves, C, & Mathias, K, 1995b “Test Driving Three 1995 Genetic Algorithms: New Test Functions and Geometric Matching”, Heuristics, 1:77–104.

    Article  MATH  Google Scholar 

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Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Watson, R.A., Hornby, G.S., Pollack, J.B. (1998). Modeling building-block interdependency. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056853

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  • DOI: https://doi.org/10.1007/BFb0056853

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

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