Emergent behavior in classifier systems

Stephanie Forrest, John H. Miller

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The paper presents examples of emergent behavior in classifier systems, focusing on symbolic reasoning and learning. These behaviors are related to global dynamical properties such as state cycles, basins of attraction, and phase transitions. A mapping is defined between classifier systems and an equivalent dynamical system (Boolean networks). The mapping provides a way to understand and predict emergent classifier system behaviors by observing the dynamical behavior of the Boolean networks. The paper reports initial results and discusses the implications of this approach for classifier systems.

Original languageEnglish (US)
Pages (from-to)213-227
Number of pages15
JournalPhysica D: Nonlinear Phenomena
Volume42
Issue number1-3
DOIs
StatePublished - Jun 1990
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Condensed Matter Physics
  • Applied Mathematics

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