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.

has been cited by the following article:

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

  AUTHORS: Yongqiang ZHANG, Huifang CHENG, Ruilan YUAN

  KEYWORDS: Improved Genetic Programming, Symbolic Regression, Software Reliability Model

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.2 No.5, December 28, 2009

  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.