---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Mathis
      foaf_name: Brede, Mathis
      foaf_surname: Brede
  dct_date: 2021^xs_gYear
  dct_language: eng
  dct_publisher: Paderborn University@
  dct_title: Implementation and Profiling of XCS in the Context of Embedded Systems@
...