Optimization Task (original) (raw)
Interactive Evolutionary Computation (IEC) community aims at reducing user's fatigue during an optimization task involving subjective criteria: a set of graphic potential solutions are simultaneously shown to a user which task is to identify most interesting solutions to the problem he had to solve. Evolutionary operators are applied to user choices expecting to produce better solutions. As traditional IEC ask the user to give a mark to each solution or to explicitly choose bests solutions with a mouse, we propose a new framework that uses in real time gaze information to predict which parts of a screen is more significant for a user. We can therefore avoid the user to explicitly choose which solutions are interesting for him. In this paper, we mainly focus on automatically ordering solutions shown on a screen given a gaze path obtained by an eye-tracker. We applied several supervised learning methods (SVM, neural networks…) on two different experiments. We obtain a formula that predict with 85% user choices. We demonstrate that decisive criterion is time spent on one solution and we show the independency between this formula and the experiment.
Sign up for access to the world's latest research.
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
Related papers
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08, 2008