Abstract
Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) capable of acquiring, evolving and re-using partial solutions. In this paper, we apply for the first time CGP and ECGP to the ones-max and order-3 deceptive problems, which are normally associated with Genetic Algorithms. Our approach uses CGP and ECGP to evolve a sequence of commands for a tape-head, which produces an arbitrary length binary string on a piece of tape. Computational effort figures are calculated for CGP and ECGP and our results compare favourably with those of Genetic Algorithms.
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References
Angeline, P.J., Pollack, J.: Evolutionary module acquisition. In: Proc. of the 2nd Annual Conference on Evolutionary Programming, pp. 154–163 (1993)
Walker, J.A., Miller, J.F.: Investigating the performance of module acquisition in cartesian genetic programming. In: Proc. of GECCO, vol. 2, pp. 1649–1656. ACM, New York (2005)
Walker, J.A., Miller, J.F.: Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems. In: Proc. of GECCO, ACM, New York (2006)
Ackley, D.H.: A connectionist Machine for Genetic Hillclimbing. Kluwer, Dordrecht (1987)
Tuson, A., Ross, P.: Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2) (1998)
Goldberg, D.E., Deb, K., Korb, B.: Messy genetic algortihms: Motivation, analysis and first results. Complex Systems 3(5) (1989)
Yu, T., Miller, J.F.: The role of neutral and adaptive mutation in an evolutionary search on the onemax problem. In: Late Breaking Papers at GECCO, pp. 512–519. AAAI, Menlo Park (2002)
Ryan, C., Nicolau, M., O’Neill, M.: Genetic algorithms uing grammatical evolution. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 278–287. Springer, Heidelberg (2002)
Nicolau, M., Ryan, C.: Linkgauge: Tackiling hard deceptive problems with a new linkage learning genetic algortihm. In: Proc. of GECCO, pp. 488–494. AAAI, Menlo Park (2002)
O’Neill, M., Brabazon, A.: mGGA:The meta-grammar genetic algorithm. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J.I., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 311–320. Springer, Heidelberg (2005)
Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
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Walker, J.A., Miller, J.F. (2007). Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming. 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_24
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DOI: https://doi.org/10.1007/978-3-540-71605-1_24
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