A Comparison of Three Fitness Prediction Strategies for Interactive Genetic Algorithms

The human fatigue problem is one of the most significant problems encountered by interactive genetic algorithms (IGA). Different strategies have been proposed to address this problem, such as easing evaluation methods, accelerating IGA convergence via speedup algorithms, and fitness prediction. This paper studies the performance of fitness prediction strategies. Three prediction schemes, the neural network (NN), the Bayesian learning algorithm (BLA), and a novel prediction method based on algorithmic probability (ALP), are examined. Numerical simulations are performed in order to compare the performances of these three schemes.