Abstract
Learning classifier system (LCSs) have the ability to solve many difficult benchmark problems, but they have to be applied individually to each separate problem. Moreover, the solutions produced, although accurate, are not compact such that important knowledge is obscured. Recently a multi-agent system has been introduced that enables multiple, different LCSs to address multiple different problems simultaneously, which reduces the need for human system set-up, recognises existing solutions and assigns a suitable LCS to a new problem. However, the LCSs do not collaborate to solve a problem in a compact or human observable manner. Hence the aim is to extract knowledge from problems by combining solutions from multiple LCSs in a compact manner that enables patterns in the data to be visualised. Results show the successful compaction of multiple solutions to a single, optimum solution, which shows important feature knowledge that would otherwise have been hidden.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bernadó, E., Llorà , X., Garrell, J.M.: XCS and GALE: a comparative study of two learning classifier systems on data mining. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS, vol. 2321, pp. 115–132. Springer, Heidelberg (2002). doi:10.1007/3-540-48104-4_8
Browne, W., Scott, D.: An abstraction algorithm for genetics-based reinforcement learning. In: Proceedings of the 7th annual conference on Genetic and evolutionary computation, pp. 1875–1882. ACM (2005)
Butz, M.V., Lanzi, P.L., Wilson, S.W.: Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. Trans. Evol. Comput. 3(12), 355–376 (2008)
Dixon, P.W., Corne, D.W., Oates, M.J.: A preliminary investigation of modified XCS as a generic data mining tool. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS, vol. 2321, pp. 133–150. Springer, Heidelberg (2002). doi:10.1007/3-540-48104-4_9
Ioannides, C., Browne, W.: Investigating scaling of an abstracted LCS utilising ternary and s-expression alphabets. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà , X., Takadama, K. (eds.) IWLCS 2006-2007. LNCS, vol. 4998, pp. 46–56. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88138-4_3
Iqbal, M., Browne, W.N., Zhang, M.: Extracting and using building blocks of knowledge in learning classifier systems. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 863–870. ACM (2012)
Iqbal, M., Browne, W.N., Zhang, M.: Evolving optimum populations with XCS classifier systems. Soft. Comput. 17(3), 503–518 (2013)
Iqbal, M., Browne, W.N., Zhang, M.: Extending learning classifier system with cyclic graphs for scalability on complex, large-scale boolean problems. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 1045–1052. ACM (2013)
Iqbal, M., Browne, W.N., Zhang, M.: Learning overlapping natured and niche imbalance boolean problems using XCS classifier systems. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1818–1825. IEEE (2013)
Iqbal, M., Browne, W.N., Zhang, M.: Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Trans. Evol. Comput. 18(4), 465–480 (2014)
Iqbal, M., Naqvi, S.S., Browne, W.N., Hollitt, C., Zhang, M.: Salient object detection using learning classifiersystems that compute action mappings. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation GECCO 2014, pp. 525–532 (2014)
Lanzi, P.L.: Mining interesting knowledge from data with the XCS classifier system. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 958–965. Morgan Kaufmann Publishers Inc. (2001)
Liu, Y., Iqbal, M., Alvarez, I., Browne, W.N.: Integration of code-fragment based learning classifier systems for multiple domain perception and learning. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2177–2184 (2016)
Orriols-Puig, A., Bernadó-Mansilla, E.: A further look at UCS classifier system. In: GECCO06, pp. 8–12 (2006)
Urbanowicz, R.J., Granizo-Mackenzie, A., Moore, J.H.: An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systems. Comput. Intell. Mag. 7(4), 35–45 (2012)
Urbanowicz, R.J., Browne, W.N.: Introduction to Learning Classifier Systems. Springer, Heidelberg (2017)
Urbanowicz, R.J., Moore, J.H.: Exstracs 2.0: description and evaluation of a scalable learning classifier system. Evol. Intell. 8(2–3), 89–116 (2015)
Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, Y., Xue, B., Browne, W.N. (2017). Visualisation and Optimisation of Learning Classifier Systems for Multiple Domain Learning. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-68759-9_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68758-2
Online ISBN: 978-3-319-68759-9
eBook Packages: Computer ScienceComputer Science (R0)