Abstract
This paper investigates the role of syntactic locality and semantic locality of crossover in Genetic Programming (GP). First we propose a novel crossover using syntactic locality, Syntactic Similarity based Crossover (SySC). We test this crossover on a number of real-valued symbolic regression problems. A comparison is undertaken with Standard Crossover (SC), and a recently proposed crossover for improving semantic locality, Semantic Similarity based Crossover (SSC). The metrics analysed include GP performance, GP code bloat and the effect on the ability of GP to generalise. The results show that improving syntactic locality reduces code bloat, and that leads to a slight improvement of the ability to generalise. By comparison, improving semantic locality significantly enhances GP performance, reduces code bloat and substantially improves the ability of GP to generalise. These results comfirm the more important role of semantic locality for crossover in GP.
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References
Banzhaf, W., Langdon, W.B.: Some considerations on the reason for bloat. Genetic Programming and Evolvable Machines 3(1), 81–91 (2002)
Beadle, L., Johnson, C.: Semantically driven crossover in genetic programming. In: Proceedings of the IEEE World Congress on Computational Intelligence, pp. 111–116. IEEE Press, Los Alamitos (2008)
Cleary, R., O’Neill, M.: An attribute grammar decoder for the 01 multi-constrained knapsack problem. In: Proceedings of the Evolutionary Computation in Combinatorial Optimization, pp. 34–45. Springer, Heidelberg (April 2005)
Costelloe, D., Ryan, C.: On improving generalisation in genetic programming. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 61–72. Springer, Heidelberg (2009)
de la Cruz Echeanda, M., de la Puente, A.O., Alfonseca, M.: Attribute grammar evolution. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 182–191. Springer, Heidelberg (2005)
Bryant, R.E.: Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers C-35, 677–691 (1986)
Ekart, A., Nemeth, S.Z.: A metric for genetic programs and fitness sharing. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J.F., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 259–270. Springer, Heidelberg (2000)
Gagne, C., Schoenauer, M., Parizeau, M., Tomassini, M.: Genetic programming, validation sets, and parsimony pressure. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 109–120. Springer, Heidelberg (2006)
Gottlieb, J., Raidl, G.: The effects of locality on the dynamics of decoder-based evolutionary search. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 283–290. ACM, New York (2000)
Hoai, N.X., McKay, R.I., Essam, D.: Representation and structural difficulty in genetic programming. IEEE Transection on Evolutionary Computation 10(2), 157–166 (2006)
Johnson, C.: Deriving genetic programming fitness properties by static analysis. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 298–308. Springer, Heidelberg (2002)
Johnson, C.: What can automatic programming learn from theoretical computer science. In: Proceedings of the UK Workshop on Computational Intelligence, University of Birmingham (2002)
Johnson, C.: Genetic programming with fitness based on model checking. In: Proceedings of the 10th European Conference on Genetic Programming (EuroGP 2002), pp. 114–124. Springer, Heidelberg (2007)
Katz, G., Peled, D.: Genetic programming and model checking: Synthesizing new mutual exclusion algorithms. In: Cha, S(S.), Choi, J.-Y., Kim, M., Lee, I., Viswanathan, M. (eds.) ATVA 2008. LNCS, vol. 5311, pp. 33–47. Springer, Heidelberg (2008)
Katz, G., Peled, D.: Model checking-based genetic programming with an application to mutual exclusion. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 141–156. Springer, Heidelberg (2008)
Langdon, W.B.: Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines 1(1), 91–1119 (2000)
Levenshtein, V.I.: Binary Codes Capable of Correcting Deletions, Insertions, and Reversa ls. Soviet Physics Doklady 10, 707 (1966)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1996)
Nguyen, Q.U., Nguyen, T.H., Nguyen, X.H., O’Neill, M.: Improving the generalisation ability of genetic programming with semantic similarity based crossover. In: Esparcia-Alcazar, A.I., Ekart, A., Silva, S., Dignum, S. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 184–195. Springer, Heidelberg (2010)
Rothlauf, F., Goldberg, D.: Redundant Representations in Evolutionary Algorithms. Evolutionary Computation 11(4), 381–415 (2003)
Rothlauf, F., Oetzel, M.: On the locality of grammatical evolution. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 320–330. Springer, Heidelberg (2006)
Silva, S., Almeida, J.: Dynamic maximum tree depth: A simple technique for avoiding bloat in tree-based gp. In: Proceedings of the 5th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1776–1787. ACM Press, New York (2003)
Uy, N.Q., Hoai, N.X., O’Neill, M.: Semantic aware crossover for genetic programming: the case for real-valued function regression. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 292–302. Springer, Heidelberg (2009)
Uy, N.Q., O’Neill, M., Hoai, N.X., McKay, B., Lopez, E.G.: Semantic similarity based crossover in GP: The case for real-valued function regression. In: Collet, P. (ed.) 9th International Conference on Evolution Artificielle. LNCS, pp. 13–24. Springer, Heidelberg (October 2009)
Vanneschi, L., Gustafson, S.: Using crossover based similarity measure to improve genetic programming generalization ability. In: GECCO 2009: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, Montreal, July 8-12, pp. 1139–1146. ACM, New York (2009)
Wong, M.L., Leung, K.S.: An induction system that learns programs in different programming languages using genetic programming and logic grammars. In: Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence (1995)
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Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, B. (2010). The Role of Syntactic and Semantic Locality of Crossover in Genetic Programming. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_54
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DOI: https://doi.org/10.1007/978-3-642-15871-1_54
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