<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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 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.

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

Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Self-adaptation of parameters in a learning classifier system ensemble machine"}

Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the...

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

Publié en ligne: 25 mars 2010

Pages: 157 - 174

DOI: https://doi.org/10.2478/v10006-010-0012-8

Mots clés
machine learning, extended classifier system, self-adaptation, adaptive parameter control

This content is open access.

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

Publié en ligne: 25 mars 2010

Pages: 157 - 174

DOI: https://doi.org/10.2478/v10006-010-0012-8

Mots clésmachine learning, extended classifier system, self-adaptation, adaptive parameter control

This content is open access.

Mots clés
machine learning, extended classifier system, self-adaptation, adaptive parameter control