Abstract
A fundamental aspect of many evolutionary approaches to synthesis of complex systems is the need to compose atomic elements into useful higher-level building blocks. However, the ability of genetic algorithms to promote useful building blocks is based critically on genetic linkage — the assumption that functionally related alleles are also arranged compactly on the genome. In many practical problems, linkage is not known a priori or may change dynamically. Here we propose that a problem’s Hessian matrix reveals this linkage, and that an eigenstructure analysis of the Hessian provides a transformation of the problem to a space where first-order genetic linkage is optimal. Genetic algorithms that dynamically transforms the problem space can operate much more efficiently. We demonstrate the proposed approach on a real-valued adaptation of Kaufmann’s NK landscapes and discuss methods for extending it to higher-order linkage.
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References
Chen, Y.-P., Goldberg, D.E. Introducing start expression genes to the linkage learning genetic algorithm. Proceedings of the Parallel Problem Solving from Nature Conference (PPSN VII). Berlin, Germany: Springer, 351–360, 2002.
Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
De Jong, K. An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975.
Goldberg, D., Korb, B., and Deb, K. Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Systems, 4:415–444, 1989.
Holland, J.H. Adaptation in Natural and Artificial Systems University of Michigan Press, Ann Arbor, 1975.
Kauffman, S. The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, 1993.
Koza J. Hierarchical genetic algorithms operating on populations of computer programs. 11th Int. joint conference on genetic algorithms, 768–774, 1989.
Lipson H., Siegelmann H.T. High Order Eigentensors as Symbolic Rules in Competitive Learning. in Hybrid Neural Systems, S. Wermter, R. Sun (Eds.) Springer, LNCS 1778, 286–297, 2002.
Mahfoud S. Niching Methods for Genetic Algorithms. IlliGAL Report No. 95001, University of Illinois at Urbana-Champaign, 77–80, 1995.
Watson, R.A., Pollack, J.B. A Computational Model of Symbiotic Composition in Evolutionary Transitions Biosystems. to appear in 2003.
Harik, G., and Goldberg, D.E. Learning linkage. Foundations of Genetic Algorithms 4:247–262, 1997.
Kargupta, H. The gene expression messy genetic algorithm. Proceedings of the 1996 IEEE International Conference on Evolutionary Computation. Nagoya University, Japan, 631–636, 1996.
Bertsekas, D.P. Non-linear Programming. Athena Scientific, Belmont, MA, 134–141, 1995.
Shanno, D.F. Conditioning of quasi-Newton methods for function minimization. Mathematics of Computation 24:647–656, 1970.
Stanley, K.O., Miikkulainen, R. Achieving High-Level Functionality Through Complexification. Proceedings of the AAAI 2003 Spring Symposium on Computational Synthesis. Stanford, CA: AAAI Press, 2003.
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Wyatt, D., Lipson, H. (2003). Finding Building Blocks through Eigenstructure Adaptation. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_23
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DOI: https://doi.org/10.1007/3-540-45110-2_23
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