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.
- Genetic Algorithm
- Fitness Landscape
- Problem Decomposition
- Uniform Crossover
- Recursive Construction
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Altenberg, L, 1995 “The Schema Theorem and Price's Theorem”, FOGA3, editors Whitley & Vose, pp 23–49, Morgan Kauffmann, San Francisco.
Deb, K & Goldberg, DE, 1989, “An investigation of Niche and Species Formation in genetic Function Optimization”, ICGA3, San Mateo, CA: Morgan Kauffman.
Deb, K & Goldberg, DE, 1992, “Sufficient conditions for deceptive and easy binary functions”, (IlliGAL Report No. 91009), University of Illinois, IL.
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.
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.
Goldberg, DE, 1989 “Genetic Algorithms in Search, Optimisation and Machine Learning”, Reading Massachusetts, Addison-Wesley.
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.
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.
Goldberg, DE, Deb, K, & Korb, B, 1989 “Messy Genetic Algorithms: Motivation, Analysis and first results”, Complex Systems, 3, 493–530.
Holland, JH, 1975 “Adaptation in Natural and Artificial Systems”, Ann Arbor, MI: The University of Michigan Press.
Holland, JH, 1993 “Royal Road Functions”, Internet Genetic Algorithms Digest v7n22.
Jones, T, 1995, Evolutionary Algorithms, Fitness Landscapes and Search, PhD dissertation, 95-05-048, University of New Mexico, Albuquerque. pp. 62–65.
Jones, T, & Forrest, S, 1995 “Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms” ICGA 6, Morgan & Kauffman.
Kauffman, SA, 1993 “The Origins of Order”, Oxford University Press.
Michalewicz, Z, 1992, “Genetic Algorithms + Data Structures = Evolution Programs” Springer-Verlag, New York, 1992.
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.
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.
Simon, HA, 1969 “The Sciences of the Artificial” Cambridge, MA. MIT Press.
Smith, RE, Forrest, S, & Perelson, A, 1993 “Searching for Diverse, Cooperative Populations with Genetic Algorithms”, Evolutionary Computation 1(2), ppl27–149.
Whitley, D, Mathias, K, Rana, S & Dzubera, J, 1995 “Building Better Test Functions”, ICGA-6, editor Eshelman, pp239–246, Morgan Kauffmann, San Francisco.
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.
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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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