Abstract
Parallel Linear Genetic Programming (PLGP) is an architecture that addresses instruction dependencies in Linear Genetic Programming (LGP). The Co-operative Coevolution (CC) methodology has previously been applied to PLGP but implementations have not been able to improve performance over vanilla PLGP. In this paper we present Hill Climbing Parallel Linear Genetic Programming (HC-PLGP) which uses a local search to discover effective combinations (blueprints) of partial solutions that are evolved in subpopulations. By introducing a new caching technique we can efficiently search over the subpopulations, and our improved fitness function combined with normalisation and blueprint elitism address some of the weaknesses of the previous approaches. Hill Climbing Parallel Linear Genetic Programming (HC-PLGP) is compared to three PLGP architectures over six datasets, and significantly outperforms them on two datasets, is comparable on three, and is slightly worse on one dataset.
This is a preview of subscription content, log in via an institution to check access.
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
Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation 5(1), 17–26 (2001)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Downey, C.: Explorations in Parallel Linear Genetic Programming. Master’s thesis, Victoria University of Wellington, New Zealand (2011)
Downey, C., Zhang, M.: Parallel Linear Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 178–189. Springer, Heidelberg (2011)
Downey, C., Zhang, M., Liu, J.: Parallel linear genetic programming for multi-class classification. Genetic Programming and Evolvable Machines 13(3), 275–304 (2012)
Fogelberg, C., Zhang, M.: Linear Genetic Programming for Multi-class Object Classification. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 369–379. Springer, Heidelberg (2005)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Gomez, F., Miikkulainen, R.: 2-d pole balancing with recurrent evolutionary networks. In: Proceedings of the International Conference on Artificial Neural Networks, pp. 425–430 (1998)
Holm, S.: A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 65–70 (1979)
Hull, J.J.: A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 550–554 (1994)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R., Streeter, M.J., Keane, M.A.: Routine high-return human-competitive automated problem-solving by means of genetic programming. Information Sciences 178(23), 4434–4452 (2008)
Moriarty, D.E., Miikkulainen, R.: Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation 5, 373–399 (1997)
Olague, G., Romero, E., Trujillo, L., Bhanu, B.: Multiclass Object Recognition Based on Texture Linear Genetic Programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 291–300. Springer, Heidelberg (2007)
Potter, M., Jong, K.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Potter, M., De Jong, K.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Schwefel, H.P., Milnner, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178(15), 2985–2999 (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
Scoble, A., Johnston, M., Zhang, M. (2012). Local Search in Parallel Linear Genetic Programming for Multiclass Classification. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-35101-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35100-6
Online ISBN: 978-3-642-35101-3
eBook Packages: Computer ScienceComputer Science (R0)