Performance and Population State Metrics for Rule-based Learning Systems

T Kovacs

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the XCS system, and demonstrate their superiority in some cases.
Translated title of the contributionPerformance and Population State Metrics for Rule-based Learning Systems
Original languageEnglish
Title of host publicationUnknown
EditorsDavid Fogel
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1781 - 1786
Number of pages5
Publication statusPublished - May 2002

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 2002 Congress on Evolutionary Computation (CEC)

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