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For real! XCS with continuous-valued inputs.

Christopher Stone1, Larry Bull

  • 1Faculty of Computing, Engineering and Mathematical Sciences University of the West of England Bristol, BS16 1QY, United Kingdom. christopher.stone@uwe.ac.uk

Evolutionary Computation
|October 16, 2003
PubMed
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This study introduces a novel interval-based representation for Learning Classifier Systems (LCS), enhancing their ability to address real-world problems beyond traditional ternary representations. The research analyzes biases in existing methods and proposes a new, more straightforward approach for improved performance.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Intelligence

Background:

  • Traditional Learning Classifier Systems (LCS) often utilize ternary representations, limiting their applicability to real-world problems with continuous or interval-based data.
  • Existing interval-based representations for LCS, such as those for XCS (eXternal Classifier System), exhibit significant representational and operator bias.
  • Benchmark problems like the real multiplexer may not accurately reflect the complexities of continuous-valued real-world scenarios.

Purpose of the Study:

  • To analyze the representational and operator bias in existing interval-based representations for LCS.
  • To propose a new, more straightforward interval-based representation for LCS.
  • To introduce the checkerboard problem as a more suitable benchmark for evaluating LCS in continuous-valued environments.

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Main Methods:

  • Analysis of two recently proposed interval-based representations for XCS, including their associated operators.
  • Development and bias analysis of a novel interval-based representation for LCS.
  • Comparison of different representations and operators using the real multiplexer and the newly proposed checkerboard problem.

Main Results:

  • Evidence of considerable representational and operator bias in existing interval-based representations for XCS was found.
  • The proposed new interval-based representation is more straightforward and its bias was analyzed.
  • Representational, operator, and sampling bias were identified as factors affecting XCS performance in continuous-valued environments.

Conclusions:

  • Interval-based representations are preferable for LCS tackling real-world problems not suited for ternary representations.
  • The proposed interval-based representation offers a more direct approach, and the checkerboard problem provides a better evaluation framework.
  • Understanding and mitigating representational, operator, and sampling biases is crucial for effective LCS in continuous-valued domains.