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 …

Motivated by the increasing complexity and operational productivity of industrial processes,
the need for efficient modeling schemes for industrial systems is highly demanded. This …

In this publication a constant optimization approach for symbolic regression is introduced to
separate the task of finding the correct model structure from the necessity to evolve the …

Tribological systems are mechanical systems that rely on friction to transmit forces. The
design and dimensioning of such systems requires prediction of various characteristic, such …

Maintaining high level of quality in hot rolling manufacturing processes is very challenging
problem to keep competitiveness in the iron and steel industrial market. Monitoring the …

Genetic programming (GP) approaches are among the state-of-the-art for symbolic
regression, the task of constructing symbolic expressions that fit well with data. To find highly …

In this contribution, we discuss data-based methods for building regression models for
predicting important characteristics of tribological systems (such as the friction coefficient) …

In recent years, Learning to Rank has not only shown effectiveness and better suitability for
modern Web Era needs, but also has proved that it outperforms traditional ranking in terms …

In this publication a constant optimization approach for symbolic regression by genetic
programming is presented. The Levenberg-Marquardt algorithm, a nonlinear, least-squares …

Genetic Programming (GP) is an important technique in evolutionary computing. There has
been extensive research and great achievement in GP and its variants. Grammar-based …