Self-adaptation of parameters in a learning classifier system ensemble machine - Troć, Maciej; Unold, Olgierd (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: machine learning; extended classifier system; self-adaptation; adaptive parameter control