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Reducing User Fatigue in Interactive Genetic Algorithms by Evaluation of Population Subsets

We present an analysis of user fatigue mitigation techniques in interactive genetic algorithms (IGAs). A user guides the evolutionary process by picking every t generations the solution the user likes best out of a small subset selected from a large population size. We explore how the selection of individuals from the population to be displayed to the user for subjective evaluation affects the convergence of the IGA. We show that we can reduce the number of evaluations performed by a user with our evaluation technique by tenfold compared to a standard IGA using a small population size. Our approach is tested on the Onemax problem in order to analyze how the IGA performance varies in a well known fitness landscape. In using the Onemax problem, we are interested in showing the ability of a user to guide the population towards areas of high fitness, rather than finding a single optimal solution. We study the effect of different subset selection strategies, different subset sizes, different choices of t, the presence or absence of collaboration, and differing amounts of noise on performance. Results show that using our fitness interpolation technique a user can effectively bias the interactive evolutionary search towards high fitness solutions despite only evaluating a small fraction of the total number of individuals.