Combating user fatigue in iGAs: Partial ordering, support vector machines, and synthetic fitness

by Llorà, X., Sastry, K., Goldberg, D.E., Gupta, A., Lakshmi, L. (2005).
Published in the ACM Genetic and Evolutionary Computation Conference (GECCO 2005), ACM press, pp. 1363–1371. Also as IlliGAL TR No 2005009. Link to the PDF

One of the daunting challenges of interactive genetic algorithms (iGAs)—genetic algorithms in which fitness measure of a solution is provided by a human rather than by a fitness function, model, or computation—is user fatigue which leads to sub-optimal solutions. This paper proposes a method to combat user fatigue by augmenting user evaluations with a synthetic fitness function. The proposed method combines partial ordering concepts, notion of non-domination from multiobjective optimization, and support vector machines to synthesize a fitness model based on user evaluation. The proposed method is used in an iGA on a simple test problem and the results demonstrate that the method actively combats user fatigue by requiring 3–7 times less user evaluation when compared to a simple iGA.