Dynamically adding sensors to the XCS in multistep problems: A sensor tagging approach

Liang Yu Chen, Po Ming Lee, Tzu-Chien Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Dynamically adding sensors to the Extended Classifier System (XCS) during its learning process in multistep problems has been demonstrated feasible by using messy coding (XCSm) and sexpressions (XCSL) as the representation of classifier conditions. XCSm and XCSL shown improved performance when new sensors were dynamically added to the agent of these systems in addition to the original available sensors during the learning process. However, these systems may suffer from overspecified problem and some logical operators (or clauses) could lead instability of the performance. Despite studies have suggested that these issues can be solved by appropriate parameter tuning, in our previous finding, we introduced a novel representation of classifier conditions for the XCS, named Sensory Tag (ST) (called XCS with ST condition, XCSSTC) to achieve the same goal as XCSm and XCSL but inherent most of the mechanisms of the XCS to solve those issues that the XCSm and XCSL encountered without any parameter tuning. The experiments of the proposed method were conducted in the multistep problems (i.e. Woods1 and Maze4). The results indicate that the XCSSTC is capable of being dynamic added additional sensors to improve performance during the learning process, and moreover, the XCSSTC shown a better performance in regard to learning speed than the other methods. Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publicationGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
EditorsSara Silva
PublisherAssociation for Computing Machinery, Inc
Pages1367-1368
Number of pages2
ISBN (Electronic)9781450334884
DOIs
StatePublished - 11 Jul 2015
Event17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015

Publication series

NameGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Conference

Conference17th Genetic and Evolutionary Computation Conference, GECCO 2015
CountrySpain
CityMadrid
Period11/07/1515/07/15

Keywords

  • Learning classifier systems
  • Machine learning
  • Scalability
  • XCS

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