Genetic programming with separability detection for symbolic regression

Genetic Programming for Symbolic Regression

Genetic programming (GP) is a supervised learning method motivated by an analogy to biological evolution. GP creates successor hypotheses by repeatedly mutating and crossovering parts of the current best hypotheses, with expectation to find a good solution in the evolution process. In this report, the task to be performed was a symbolic regression problem, which is to find the symbolic function...

Sequential Symbolic Regression with Genetic Programming

This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transforma...

Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming

In this paper we continue study on the Stepwise Adaptation of Weights (saw) technique. Previous studies on constraint satisfaction and data classification have indicated that saw is a promising technique to boost the performance of evolutionary algorithms. Here we use saw to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance o...

Genetic Programming for Symbolic Regression of Chemical Process Systems

The novel evolutionary artificial intelligence formalism namely, genetic programming (GP) a branch of genetic algorithms is utilized to develop mathematical models based on input-output data, instead of conventional regression and neural network modeling techniques which are commonly used for this purpose. This paper summarizes the available MATLAB toolboxes and their features. Glucose to gluco...

Symbolic regression of thermophysical model using genetic programming