Self-adaptation of parameters in a learning classifier system ensemble machine

Maciej Troć; Olgierd Unold

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 1, page 157-174
  • ISSN: 1641-876X

Abstract

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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 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.

How to cite

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Maciej Troć, and Olgierd Unold. "Self-adaptation of parameters in a learning classifier system ensemble machine." International Journal of Applied Mathematics and Computer Science 20.1 (2010): 157-174. <http://eudml.org/doc/207971>.

@article{MaciejTroć2010,
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. 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.},
author = {Maciej Troć, Olgierd Unold},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {machine learning; extended classifier system; self-adaptation; adaptive parameter control},
language = {eng},
number = {1},
pages = {157-174},
title = {Self-adaptation of parameters in a learning classifier system ensemble machine},
url = {http://eudml.org/doc/207971},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Maciej Troć
AU - Olgierd Unold
TI - Self-adaptation of parameters in a learning classifier system ensemble machine
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 1
SP - 157
EP - 174
AB - 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 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.
LA - eng
KW - machine learning; extended classifier system; self-adaptation; adaptive parameter control
UR - http://eudml.org/doc/207971
ER -

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