Symbolic regression using α- β operators and estimation of distribution algorithms: preliminary results

• Authors: Luis M. Torres-Treviño
• Venue: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
• Year: 2011

Modeling processes is an important task in engineering; however, the generation of models using only experimental data is not a straightforward problem. Linear regression, neural networks, and other approaches have been used for this purpose; nevertheless, a mathematical description is desirable specially when an optimization is required. Symbolic regression has been used for generating equations considering only experimental data. In this paper, two new operators are proposed to represent a mathematical model of a process. These operators simplified the way for representing equations making possible its use as a symbolic regression. The correct model is generated selecting the appropriate operators and parameters using an evolutionary algorithm like the estimation of distribution algorithms. As a preliminary results, three cases are used to illustrated the performance of the proposed approach. The results indicates that the use of these α, β operators are a promising way to apply symbolic regression to model complex process.