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The number of researchers used non-linear and linear regression analysis for the investigation of the relationship between the response and process parameters. It is used for the modeling of the process, where one response is affected by the number of parameters (Cetin et al. 2011; Sharma et al. 2019). In the present work, linear regression relationship (i.e. mathematical model) as in Eq. 2 was derived for the investigation of porosity.

Genetic programming (GP) is a random probability-based search method to evaluate the optimum solution of any objective function. Holland (1975) proposed genetic algorithms as a program, which evaluates the solution of problem by the natural evolutionary process in the early seventies. De Jong (1975) extends the algorithm from program based to function based and optimization of function-based optimization start. The detailed optimization of any mathematical model with the help of GA was started by Goldberg (1989). Genetic programming is used to solve a mathematical model in which a response affected by the number of process parameters. The upper and lower bounds of process parameters were defined during the statement of the problem (Li et al. 2013; Sharma et al. 2015). Initially, one objective function was prepared for maximization problem. Then this function is solved by the different combination of mutation, cross-over function and selection. Regression equation (i.e. Eq. 2) given by regression analysis was further solved by using genetic programming. The lower and upper bound of the process parameters is defined from Eqs. 35.