Abstract Evolutionary Ensemble Learning (EEL) provides a general approach for scaling
evolutionary learning algorithms to increasingly complex tasks. This is generally achieved …

Scalability issues might prevent Genetic Algorithms from being applied to real world
problems. Exploiting parallelisation in the cloud might be an affordable approach to getting …

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research
on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have …

Designing a controller is typically an iterative process during which engineers must assess
the performance of a design through time-consuming simulations; this becomes even more …

Industrial separations of near-azeotropic chemicals, species with very similar boiling points,
are energy-and capital-intensive. Adsorption-based processes can energy-efficiently …

Ensemble learning is a powerful paradigm that has been used in the top state-of-the-art
machine learning methods like Random Forests and XGBoost. Inspired by the success of …

Predicting seismic damage spectra, capturing both structural and earthquake features, is
useful in performance-based seismic design and quantifying the potential seismic damage …

Most traditional genetic programming methods that handle symbolic regression are random
algorithms without memory and direction. They repeatedly search for the same or similar …

To predict the maximum heating load induced by shock–shock interaction more reliably and
accurately, the geometrical scale of the overall wave configuration of shock–shock …

Learning ensembles by bagging can substantially improve the generalization performance
of low-bias, high-variance estimators, including those evolved by Genetic Programming …