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.
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
Protein structures prediction (PSP) is a computationally complex problem. Simplified models of the protein molecule (such as the HP Model) and the use of evolutionary algorithms (EAs) are among the most investigated techniques for PSP. However, the evaluation of a structure represented by the HP model considers only the number of hydrophobic contacts, which doesn’t
Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve
This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to