Abstract
A new model of learning classifier system is introduced to explore continuous-valued environment. The approach applies the real-valued anticipatory classifier system (rACS). In order to handle real-valued inputs effectively, the ternary representation has been replaced by an approach where the interval of real numbers is represented by a natural number. The rACS model has been tested on the 1D linear corridor and the 2D continuous gridworld environments. We show that modified ACS can evolve compact populations of classifiers which represent the optimal solution to the continuous problem.
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Unold, O., Mianowski, M. (2016). Real-Valued ACS Classifier System: A Preliminary Study. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_19
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DOI: https://doi.org/10.1007/978-3-319-26227-7_19
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