Function sequence genetic programming

Authors:
Shixian Wang;Yuehui Chen;Peng Wu
Affiliations:
Computational Intelligence Lab., School of Information Science and Engineering, University of Jinan, Jinan, Shandong, P.R. China;Computational Intelligence Lab., School of Information Science and Engineering, University of Jinan, Jinan, Shandong, P.R. China;Computational Intelligence Lab., School of Information Science and Engineering, University of Jinan, Jinan, Shandong, P.R. China
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

Genetic Programming(GP) can obtain a program structure to solve complex problem. This paper presents a new form of Genetic Programming, Function Sequence Genetic Programming (FSGP). We adopt function set like Genetic Programming, and define data set corresponding to its terminal set. Besides of input data and constants, data set include medium variables which are used not only as arguments of functions, but also as temporary variables to store function return value. The program individual is given as a function sequence instead of tree and graph. All functions run orderly. The result of executed program is the return value of the last function in the function sequences. This presentation is closer to real handwriting program. Moreover it has an advantage that the genetic operations are easy implemented since the function sequence is linear. We apply FSGP to factorial problem and stock index prediction. The initial simulation results indicate that the FSGP is more powerful than the conventional genetic programming both in implementation time and solution accuracy.