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.
Keywords
- Genetic Algorithm
- Fitness Landscape
- Problem Decomposition
- Uniform Crossover
- Recursive Construction
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.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/BFb0056853
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65078-2
Online ISBN: 978-3-540-49672-4
eBook Packages: Springer Book Archive