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ABSTRACT

This paper attempts to extend the XCS research by analyzing the impact of information exchange between XCS agents on classifier performance. Two types of information are exchanged and combined to improve classification performance. The first uncovers information contained in the signal patterns of collections of Homogeneous XCS classifiers. This information is used to determine which subsets of the state-space the XCS can be expected to be accurately classified. The second combines the results of XCS agents that are each tasked to solve different portions of the original problem. Results on the multiplexer (6, 11) indicate that given accurate problem domain assumptions, the Collective Behavior (CB-HXCS) method shows promise. Results show - at least in simulated multiplexer environments - that the HXCS is able to solve a well defined problem with less data than an individual XCS. This approach seems very promissing in real-world applications where data is incomplete, expensive or unreliable such as in financial or medical domains.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Experimentation

Keywords:
CB-HXCS, XCS, classifier, collective behavior, information exchange, multiplexer, voting