Reducing human fatigue in interactive evolutionary computation through fuzzy systems and machine learning systems

Raffi Kamalian, Eric Yeh, Ying Zhang, Alice M. Agogino, Hideyuki Takagi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

We describe two approaches to reducing human fatigue in Interactive Evolutionary Computation (IEC). A predictor function is used to estimate the human user's score, thus reducing the amount of effort required by the human user during the evolution process. The fuzzy system and four machine learning classifier algorithms are presented. Their performance in a real-world application, the IEC-based design of a micromachine resonating mass, is evaluated. The fuzzy system was composed of four simple rules, but was able to accurately predict the user's score 77% of the time on average. This is equivalent to a 51% reduction of human effort compared to using IEC without the predictor. The four machine learning approaches tested were k-nearest neighbors, decision tree, AdaBoosted decision tree, and support vector machines. These approaches achieved good accuracy on validation tests, but because of the great diversity in user scoring behavior, were unable to achieve equivalent results on the user test data.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Fuzzy Systems
Pages678-684
Number of pages7
DOIs
Publication statusPublished - 2006
Event2006 IEEE International Conference on Fuzzy Systems - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2006 IEEE International Conference on Fuzzy Systems
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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