Abstract
This paper proposes a new approach to Genetic Programming (GP). In traditional GP, recombination can cause frequent disruption of building-blocks or mutation can cause abrupt changes in the semantics. To overcome these difficulties, we supplement traditional GP with a recovery mechanism of disrupted building-blocks. More precisely, we integrate the structural search of traditional GP with a local hill-climbing search, using a relabeling procedure. This integration allows us to extend GP for Boolean and numerical problems. We demonstrate the superior effectiveness of our approach with experiments in Boolean concept formation and symbolic regression.
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References
Armstrong, W.W. and Gecsei, J. Adaptation Algorithms for Binary Tree Networks, IEEE TR. SMC, SMC-9, No.5, 1979
Armstrong, W.W. Learning and Generalization in Adaptive Logic Networks, Artificial Neural Networks, (T.Kohonen eds.), Elsevier Science Pub., 1991
Armstrong, W.W., Dwelly, A., Liang, J., Lin,D., and Reynolds,S., Some Results concerning Adaptive Logic Networks, unpublished manuscript, Sept. 16, 1991
Barzdins, J.M., and Barzdins, G.J., Rapid Construction of Algebraic Axioms from Samples, Theoretical Computer Science, vol.90, 1991
Holland,J.H. Adaptation in natural and artificial systems, University of Michigan Press, 1975
Iba, H., Kurita, T., deGaris, H. and Sato, T. System Identification using Structured Genetic Algorithms, in Proc. of 5th International Joint Conference on Genetic Algorithms, 1993
Iba, H., Niwa, T., deGaris, H. and Sato, T. Evolutionary Learning of Boolean Concepts: An Empirical Study, ETL-TR-93-25, 1993
Iba, H., deGaris, H. and Sato, T. System Identification Approach to Genetic Programming, ETL-TR94-2, in Proc. of IEEE World Congress on Computational Intelligence(WCCI94), 1994
Iba, H., deGaris, H. and Sato, T. Genetic Programming using a Minimum Description Length Principle, in Advances in Genetic Programming, (ed. Kenneth E. Kinnear, Jr.), MIT Press, 1994
Ivakhnenko, A. G. Polynomial Theory of Complex Systems, IEEE Tr. SMC, vol.SMC-1, no.4, 1971
Janikow, C.Z., A Knowledge-Intensive Genetic Algorithm for Supervised Learning, Machine Learning, vol.13, 1993
Koza, J. Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems, Report No. STAN-CS-90-1314, Dept. of Computer Science, Stanford Univ., 1990
Koza, J. Genetic Programming, On the Programming of Computers by means of Natural Selection, MIT Press, 1992
Koza, J. Genetic Programming II: Automatic Discovery of Reusable Subprograms, MIT Press, 1994 (in press)
Langley, P., and Zytkow, J. M., Data-driven Approaches to Empirical Discovery, Artificial Intelligence, vol.40, 1989
Louis, S.J. and Rawlins, G.J.E., Pareto Optimality, GA-easiness and Deception, In Proc. of 5th International Joint Conference on Genetic Algorithms (ICGA93), 1993
Schaffer, J.D. and Eshelman, L.J., On Crossover as an Evolutionarily Viable Strategy, In Proc. of 4th International Joint Conference on Genetic Algorithms (ICGA91), 1991
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© 1994 Springer-Verlag Berlin Heidelberg
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Iba, H., de Garis, H., Sato, T. (1994). Genetic Programming with local hill-climbing. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_274
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DOI: https://doi.org/10.1007/3-540-58484-6_274
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