- Tytuł:
- Self-adaptation of parameters in a learning classifier system ensemble machine
- Autorzy:
-
Troć, M.
Unold, O. - Powiązania:
- https://bibliotekanauki.pl/articles/907767.pdf
- Data publikacji:
- 2010
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
komputerowe uczenie się
system klasyfikujący
sterowanie adaptacyjne
sterowanie parametryczne
machine learning
extended classifier system
self-adaptation
adaptive parameter control - Opis:
- 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. 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 XCS-based 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 157-174
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł