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
Heart attack preceded by ischemia is responsible for many deaths worldwide. Thus, the detection of ischemic cardiac areas is very important not only to help the prevention of that mortal disease but also for teaching/learning purposes. This work presents the results of a new approach for ischemic region detection in rat heart photo. Such an
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 investigate how to efficiently apply Approximate-Karush–Kuhn–Tucker proximity measures as stopping criteria for optimization algorithms that do not generate approximations to Lagrange multipliers. We prove that the KKT error measurement tends to zero when approaching a solution and we develop a simple model to compute the KKT error measure requiring only the