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

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DOI: 10.4236/jsea.2009.25047    4,624 Downloads   8,803 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Y. ZHANG, H. CHENG and R. YUAN, "Improved Genetic Programming Algorithm Applied to Symbolic Regression and Software Reliability Modeling," Journal of Software Engineering and Applications, Vol. 2 No. 5, 2009, pp. 354-360. doi: 10.4236/jsea.2009.25047.

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