Improved Genetic Programming Algorithm Applied to Symbolic Regression and Software Reliability Modeling

Yongqiang ZHANG, Huifang CHENG, Ruilan YUAN

.

DOI: 10.4236/jsea.2009.25047                

Abstract

The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first inves-tigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operation. The IGP is examined in two domains and the results suggest that the IGP is more effective and more efficient than the canonical one applied in different domains.

Keywords

Improved Genetic Programming, Symbolic Regression, Software Reliability Model

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	H. Wright, “Introduction to Genetic Programming,” USA: University of Montana, CS555/495:1-2, 2002.
[2]	Wolfgang, N. Peter, E. K. Robert and D. F. Frank, “Ge-netic Programming: an introduction on the automatic evolution of computer programs and its applications,” San Francisco, Calif: Morgan Kaufmann Publishers: Heidelberg: Dpunkt-verlag. Subject: Genetic Program-ming (Computer science): ISBN: 1-55860-510-X, 1998.
[3]	J. R. Koza, “Genetic Programming Ⅱ: automatic discov-ery of reusable programs” , Cambridge M A :M IT Press, 1994.
[4]	J. H. Holland, “Adaptation in natural and artificial Sys-tems,” MIT Press, 1992.
[5]	R. V. Silvia and P. Aurora, “A grammar—guided Genetic Programming framework configured for data mining and software testing,” International Journal of Software En-gineering and Knowledge Engineering, Vol. 16, No. 2, pp. 245–267, 2006.
[6]	W. Banzhaf, P. Nordin, R. E. Keller and F. D. Francone, “Genetic Programming-an introduction on the automatic evolution of computer programs and its application,” Morgan Kaufmann Publishers, Inc, 1998.
[7]	J. L. Bradley, M. Sudhakar and P. Jens, “Program optimi-zation for faster genetic programming,” In Genetic Pro-gramming-GP’98, pp. 202–207, Madison, Wisconsin, July 1998.
[8]	M. D. Kramer and D. Zhang, “GAPS: A genetic pro-gramming system,” The Twenty-Fourth Annual Interna-tional Computer Software and Applications Conference, pp. 614–619, 2000.
[9]	A. P. Fraser, “Genetic Programming in C++[R],” Cyber-netics Research Institute, TR0140, University of Sanford, 1994.
[10]	A. Augusto, “symbolic regression via Genetic Program-ming,” VI Brazilian Symposium on Neural Network, pp. 173–178, 2000.
[11]	T. Walter, “Recombination, selection, and the genetic construction of computer programs,” PhD thesis. Faculty of the Graduate School, University of Southern California, Canoga Patch, California, USA, April 1994.
[12]	Y. Gong and Q. Zhou, “A software test report SRTP,” Armored Force Engineering Institute, China, 1995.
[13]	X. Huang, “Software reliability, safety and quality assur-ance,” Beijing: Electronics Industry Press, Vol. 10, pp. 9–20, 15–17, 86–112, 2002.