thesis Mathis Brede author Marco Platzner supervisor Tim Hansmeier supervisor 78 department SFB 901 - Subproject C2 project SFB 901 - Project Area C project SFB 901 project This bachelor thesis presents a C/C++ implementation of the XCS algorithm for an embedded system and profiling results concerning the execution time of the functions. These are then analyzed in relation to the input characteristics of the examined learning environments and compared with related work. Three main conclusions can be drawn from the measured results. First, the maximum size of the population of the classifiers influences the runtime of the genetic algorithm; second, the size of the input space has a direct effect on the execution time of the matching function; and last, a larger action space results in a longer runtime generating the prediction for the possible actions. The dependencies identified here can serve to optimize the computational efficiency and make XCS more suitable for embedded systems. Paderborn University2021 eng yes Brede M. <i>Implementation and Profiling of XCS in the Context of Embedded Systems</i>. Paderborn: Paderborn University; 2021. M. Brede, <i>Implementation and Profiling of XCS in the Context of Embedded Systems</i>. Paderborn: Paderborn University, 2021. Brede, Mathis. <i>Implementation and Profiling of XCS in the Context of Embedded Systems</i>. Paderborn: Paderborn University, 2021. Brede, M. (2021). <i>Implementation and Profiling of XCS in the Context of Embedded Systems</i>. Paderborn: Paderborn University. @book{Brede_2021, place={Paderborn}, title={Implementation and Profiling of XCS in the Context of Embedded Systems}, publisher={Paderborn University}, author={Brede, Mathis}, year={2021} } M. Brede, Implementation and Profiling of XCS in the Context of Embedded Systems, Paderborn University, Paderborn, 2021. Brede, Mathis. <i>Implementation and Profiling of XCS in the Context of Embedded Systems</i>. Paderborn University, 2021. 224832021-06-21T09:35:03Z2022-01-06T06:55:33Z