@misc{Troć_Maciej_Self-adaptation, author={Troć, Maciej and Unold, Olgierd}, howpublished={online}, publisher={Zielona Góra: Uniwersytet Zielonogórski}, language={eng}, abstract={Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components.}, abstract={A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCSbased ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.}, type={artykuł}, title={Self-adaptation of parameters in a learning classifier system ensemble machine}, keywords={machine learning, extended classifier system, self-adaptation, adaptive parameter control}, }