Empirical modeling using symbolic regression via postfix Genetic Programming

Developing mathematical model of a process or system from experimental data is known as empirical modeling. Traditional mathematical techniques are unsuitable to solve empirical modeling problems due to their nonlinearity and multimodality. So, there is a need of an artificial expert that can create model from experimental data. In this paper, we explored the suitability of Neural Network (NN) and symbolic regression via Genetic Programming (GP) to solve empirical modeling problems and conclude that symbolic regression via GP can deal efficiently with these problems. This paper aims to introduce a novel GP approach to symbolic regression for solving empirical modeling problems. The main contribution includes: (i) a new method of chromosome representation (postfix based) and evaluation (stack based) to reduce space-time complexity of algorithm (ii) comparison of our approach with Gene Expression Programming (GEP), a GP variant (iii) algorithms for generating valid chromosomes (in postfix notation) and identifying non-coding region of chromosome to improve efficiency of evolutionary process. Experimental results showed that empirical modeling problems can be solved efficiently using symbolic regression via postfix GP approach.