Evolutionary Algorithm for Uncertain Evaluation Function

Abstract

This paper proposes the evolutionary algorithm (EA) for the uncertain evaluation function in which fitness values change even with the same input. In detail, the proposed method employs the probability model to acquire the appropriate attributes that can drive the good solutions. To investigate the effectiveness of the proposed method, we apply it to sleep stage estimation problem where an accuracy of sleep stage estimation changes even in the same estimation filter (correspondingly the solutions). The experimental results have revealed the following implications: (i) The proposed method succeeded to acquire the robust estimation filters which stably derive a high accuracy of the sleep stage estimation; (ii) in detail, the proposed method with the roulette selection shows higher performance than the one with the random selection; and (iii) the proposed method shows high performance and robustness to the different days in comparison with the conventional sleep stage estimation method.