Abstract
Layered learning is a decomposition and reuse technique that has proved to be effective in the evolutionary solution of difficult problems. Although previous work has integrated it with genetic programming (GP), much of the application of that research has been in relation to multi-agent systems. In extending this work, we have applied it to more conventional GP problems, specifically those involving Boolean logic. We have identified two approaches which, unlike previous methods, do not require prior understanding of a problem’s functional decomposition into sub-goals. Experimentation indicates that although one of the two approaches offers little advantage, the other leads to solution-finding performance significantly surpassing that of both conventional GP systems and those which incorporate automatically defined functions.
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References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Koza, J.R.: Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming. In: Proc. 5th International Conf. Genetic Algorithms (ICGA-93), pp. 295-302 (1993)
Rosca, J.P., Ballard, D.H.: Hierarchical Self-Organization in Genetic Programming. In: Proc. 11th International Conf. on Machine Learning, pp. 251–258. Morgan Kaufmann, San Francisco (1994)
Angeline, P.J., Pollack, J.: Evolutionary Module Acquisition. In: Proc. 2nd Annual Conf. on Evolutionary Programming, La Jolla, CA, pp. 154-163 (1993)
Angeline, P.J., Pollack, J.: Coevolving High-Level Representations. In: Langton, C.G. (ed.) Artificial Life III, pp. 55–71. Addison-Wesley, London (1994)
Rosca, J.P., Ballard, D.H.: Discovery of Subroutines in Genetic Programming. In: Angeline, P., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming 2, pp. 177–202. MIT Press, Cambridge (1996)
Roberts, S.C., Howard, D., Koza, J.R.: Evolving Modules in Genetic Programming by Subtree Encapsulation. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 160–175. Springer, Heidelberg (2001)
Miller, J.F., Thomson, P.: A Developmental Method for Growing Graphs and Circuits. In: Proc. 5th International Conf. on Evolvable Systems, Trondheim, Norway, pp. 93-104 (2003)
Walker, J.A., Miller, J.F.: Evolution and Acquisition of Modules in Cartesian Genetic Programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S.M., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 187–197. Springer, Heidelberg (2004)
Walker, J.A., Miller, J.F.: Improving the Performance of Module Acquisition in Cartesian Genetic Programming. In: Beyer, H-G., O’Reilly, U-M. (eds.) Proc. GECCO 2005, pp. 1649–1656. ACM Press, New York (2005)
Stone, P., Veloso, M.: Layered Learning. In: Proc. 17th International Conf. on Machine Learning, pp. 369–381. Springer, Heidelberg (2000)
de Garis, H.: Genetic Programming: Building Artificial Nervous Systems Using Genetically Programmed Neural Network Modules. In: Porter, B.W., et al. (eds.): Proc. Seventh International Conf. on Machine Learning (ICML-90), pp. 132-139 (1990)
Asada, M., Noda, S., Tawaratsumida, S., Hosoda, K.: Purposive Behaviour Acquisition for a Real Robot by Vision-Based Reinforcement Learning. Machine Learning 23, 279–303 (1998)
Gustafon, S.M.: Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. M.S. Thesis, Dept. of Computing and Information Sciences, Kansas State University, USA (2000)
Gustafon, S.M., Hsu, W.H.: Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 291–301. Springer, Heidelberg (2001)
Hsu, W.H., Gustafon, S.M: Genetic Programming and Multi-Agent Layered Learning by Reinforcements. In: Proc. GECCO 2002, New York, NY, USA, pp. 764-771 (2002)
Hsu, W.H., Harmon, S.J., Rodriguez, E., Zhong, C.: Empirical Comparison of Incremental Reuse Strategies in Genetic Programming for Keep-Away Soccer. In: GECCO 2004 late-breaking papers (2004)
Tuan-Hao, H., et al.: Solving Symbolic Regression Problems Using Incremental Evaluation in Genetic Programming. In: Proc. 2006 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, pp. 7487–7494. IEEE Computer Society Press, Los Alamitos (2006)
Dolin, B., Arenas, M.G., Merelo, J.J.: Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VII. LNCS, vol. 2439, pp. 142–152. Springer, Heidelberg (2002)
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Jackson, D., Gibbons, A.P. (2007). Layered Learning in Boolean GP Problems. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_14
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DOI: https://doi.org/10.1007/978-3-540-71605-1_14
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