Abstract
Being able to exploit modularity in genetic programming (GP) is an open issue and a promising vein of research. Previous work has identified a variety of methods of finding and using modules, but little is reported on how the modules are being used in order to yield the observed performance gains. In this work, multiple methods for identifying modules are applied to some common, dynamic benchmark problems. Results show there is little difference in the performance of the approaches. However, trends in how modules are used and how “good” individuals use these modules are seen. These trends indicate that discovered modules can be used frequently and by good individuals. Further examination of the modules uncovers that useful as well as unhelpful modules are discovered and used frequently. The results suggest directions for future work in improving module manipulation via crossover and mutation and module usage in the population.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Angeline, P.J., Pollack, J.B.: The evolutionary induction of subroutines. In: Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pp. 236–241. Lawrence Erlbaum, Bloomington (1992)
Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments. Springer (2009)
Harper, R., Blair, A.: Dynamically defined functions in grammatical evolution. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 9188–9195. IEEE Press, Vancouver (2006)
Kashtan, N., Parter, M., Dekel, E., Mayo, A.E., Alon, U.: Extinctions in heterogeneous environments and the evolution of modularity. Evolution 63, 1964–1975 (2009)
Kashtan, N., Mayo, A.E., Kalisky, T., Alon, U.: An analytically solvable model for rapid evolution of modular structure. PLoS Comput. Biol. 5(4), e1000355 (2009)
Kashtan, N., Noor, E., Alon, U.: Varying environments can speed up evolution. Proceedings of the National Academy of Sciences 104(34), 13711–13716 (2007)
Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press (1992)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Miller, J.F., Job, D., Vassilev, V.K.: Principles in the evolutionary design of digital circuitspart ii. Genetic Programming and Evolvable Machines 1, 259–288 (2000), doi:10.1023/A:1010066330916
Murphy, E., O’Neill, M., Brabazon, A.: A comparison of ge and tage in dynamic environments. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1387–1394. ACM, New York (2011)
O’Neill, M., Nicolau, M., Brabazon, A.: Dynamic environments can speed up evolution with genetic programming. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2011, pp. 191–192. ACM, New York (2011)
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers (2003)
O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genetic Programming and Evolvable Machines 11, 339–363 (2010), doi:10.1007/s10710-010-9113-2
Rosca, J.P., Ballard, D.H.: Discovery of subroutines in genetic programming, pp. 177–201. MIT Press, Cambridge (1996)
Swafford, J.M., Hemberg, E., O’Neill, M., Nicolau, M., Brabazon, A.: A non-destructive grammar modification approach to modularity in grammatical evolution. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1411–1418. ACM, Dublin (2011)
Swafford, J.M., Nicolau, M., Hemberg, E., O’Neill, M., Brabazon, A.: Comparing methods for module identification in grammatical evolution. In: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2012. ACM, Philadelphia (2012)
Walker, J., Miller, J.: The automatic acquisition, evolution and reuse of modules in cartesian genetic programming. IEEE Transactions on Evolutionary Computation 12(4), 397–417 (2008)
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
Swafford, J.M., Hemberg, E., O’Neill, M., Brabazon, A. (2012). Analyzing Module Usage in Grammatical Evolution. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-32937-1_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32936-4
Online ISBN: 978-3-642-32937-1
eBook Packages: Computer ScienceComputer Science (R0)