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Journal of Information Science and Engineering, Vol. 23 No. 2, pp. 605-616 (March 2007)

A Comparison of Three Fitness Prediction Strategies for Interactive Genetic Algorithms*

Leuo-Hong Wang
Evolutionary Computation Laboratory
Department of Information Management
Aletheia University
Tamsui, 251 Taiwan

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.

Keywords: interactive genetic algorithm, human fatigue problem, fitness prediction, algorithmic probability, product design

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Received September 2, 2005; revised January 18 & March 9, 2006; accepted April 10, 2006.
Communicated by Pau-Choo Chung.
* The preliminary version of this paper was presented in 8th Joint Conference on Information Sciences, Salt Lake City Marriott-City Center, July 21-25th, 2005.