Constant optimization in symbolic regression is an important task addressed by several researchers. It has been demonstrated that continuous optimization techniques are adequate to find good values for the constants by minimizing the prediction error. In this paper, we evaluate several continuous optimization methods that can be used to perform constant optimization in symbolic regression.
The compressive strength of high-performance concrete (HPC) can be predicted by a nonlinear function of the proportions of its components. However, HPC is a complex material, and finding that nonlinear function is not trivial. Many distinct techniques such as traditional statistical regression methods and machine learning methods have been used to solve this task, reaching
Linear Genetic Programming (LGP) is an Evolutionary Computation algorithm, inspired in the Genetic Programming (GP) algorithm. Instead of using the standard tree representation of GP, LGP evolves a linear program, which causes a graph-based data flow with code reuse. LGP has been shown to outperform GP in several problems, including Symbolic Regression (SReg), and to
In this paper, we compare a basic linear genetic programming (LGP) algorithm against several LGP variants, proposed by us, on two sets of symbolic regression benchmarks. We evaluated the influence of methods to control bloat, investigated these techniques focused in growth of effective code, and examined an operator to consider two successful individuals as modules
This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus