Abstract
Preferential language biases which are introduced when using Tree-Adjoining Grammars in Grammatical Evolution affect the distribution of generated derivation structures, and as such, present difficulties when designing initialisation methods. Similar initial populations allow for a fairer comparison between different GP methods. This work proposes methods for dealing with these biases and examines their effect on performance over four well known benchmark problems. In addition, a comparison is performed with a previous study that did not employ similar phenotype distributions in their initial populations. It is found that the use of this form of initialisation has a positive effect on performance.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Daida, J.M., Ampy, D.S., Ratanasavetavadhana, M., Li, H., Chaudhri, O.A.: Challenges with verification, repeatability, and meaningful comparison in genetic programming: Gibson’s magic. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1851–1858. Morgan Kaufmann, Orlando (1999)
Harper, R.: GE, explosive grammars and the lasting legacy of bad initialisation. In: IEEE Congress on Evolutionary Computation (CEC 2010). IEEE Press, Barcelona (2010)
Nguyen, X.H., McKay, R.I., Abbass, H.A.: Tree adjoining grammars, language bias, and genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 335–344. Springer, Heidelberg (2003)
Hoai, N.-X., McKay, R.I(B.), Essam, D.L., Abbass, H.A.: Toward an Alternative Comparison between Different Genetic Programming Systems. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 67–77. Springer, Heidelberg (2004)
Joshi, A.: Tree adjoining grammars: How much context-sensitivity is required to provide reasonable structural descriptions, ch. 6, pp. 205–250. Cambridge University Press, New York (1985)
Joshi, A., Schabes, Y.: Tree-Adjoining Grammars. In: Handbook of Formal Languages, Beyond Words, vol. 3, pp. 69–123 (1997)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Kroch, A., Joshi, A.: The Linguistic Relevance of Tree Adjoining Grammar, Technical Report, University Of Pennsylvania (1985)
Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation 4(3), 274–283 (2000)
McKay, R., Hoai, N., Whigham, P., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. Genetic Programming and Evolvable Machines 11, 365–396 (2010)
Murphy, E., O’Neill, M., Brabazon, A.: Examining Mutation Landscapes In Grammar Based Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 130–141. Springer, Heidelberg (2011)
Murphy, E., O’Neill, M., Galvan-Lopez, E., Brabazon, A.: Tree-adjunct grammatical evolution. In: 2010 IEEE World Congress on Computational Intelligence, pp. 4449–4456. IEEE Computational Intelligence Society, IEEE Press, Barcelona, Spain (2010)
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language. Genetic programming, vol. 4. Kluwer Academic Publishers (2003)
Whigham, P.A.: Grammatical Bias for Evolutionary Learning. Ph.D. thesis, School of Computer Science, University College, University of New South Wales, Australian Defence Force Academy, Canberra, Australia (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Murphy, E., Hemberg, E., Nicolau, M., O’Neill, M., Brabazon, A. (2012). Grammar Bias and Initialisation in Grammar Based Genetic Programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-29139-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29138-8
Online ISBN: 978-3-642-29139-5
eBook Packages: Computer ScienceComputer Science (R0)