In this paper we analyze the effects of using nonlinear least squares for parameter
identification of symbolic regression models and integrate it as local search mechanism in …

When learning from high-dimensional data for symbolic regression (SR), genetic
programming (GP) typically could not generalize well. Feature selection, as a data …

Allowing the population size to variate during the evolution can bring advantages to
evolutionary algorithms (EAs), retaining computational effort during the evolution process …

Geometric semantic genetic programming (GSGP) is a well-known variant of genetic
programming (GP) where recombination and mutation operators have a clear semantic …

For all supervised learning problems, where the quality of solutions is measured by a
distance between target and output values (error), geometric semantic operators of genetic …

Geometric semantic genetic programming (GSGP) and linear scaling (LS) have both,
independently, shown the ability to outperform standard genetic programming (GP) for …

Abstract Semantics has become a key topic of research in Genetic Programming (GP).
Semantics refers to the outputs (behaviour) of a GP individual when this is run on a dataset …

Innovation performance of regional innovation systems can serve as an important tool for
policymaking to identify best practices and provide aid to regions in need. Accurate …

The electricity market is a complex, evolutionary, and dynamic environment. Forecasting
electricity prices is an important issue for all electricity market participants. In this study, we …

This work deals with the analysis and prediction of the behavior of a gas turbine (GT), the
Mitsubishi single shaft Turbo-Generator Model MS6001, which has a 30 MW generation …