Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Analyzing active interactive genetic algorithms using visual analytics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Reducing shoulder-surfing by using gaze-based password entry
Proceedings of the 3rd symposium on Usable privacy and security
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
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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.