Investigation of Linear Genetic Programming Techniques for Symbolic Regression

In this paper, we investigate some variants of a basic linear genetic programming (LGP) algorithm in the problem of symbolic regression. We explore the effects of using techniques to control bloat and to privilege a greater percentage of effective code in the population, individually, and examine its possibility of producing better solutions. We also test the effects and performance of an operator that considers two successful individuals as sub functions and join them into a new individual. We conduct experiments and discuss what effects each variant introduces to the evolution and its chance of producing better solutions.