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Abstract--We describe two approaches to reducing human fatigue in Interactive Evolutionary Computation (IEC). A
 

Summary: 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.
I. INTRODUCTION
e present an improvement on conventional Interactive
Evolutionary Computation (IEC) as applied to the field

  

Source: Agogino, Alice M. - Department of Mechanical Engineering, University of California at Berkeley

 

Collections: Engineering