Noisy optimization is currently receiving increasing popularity for its widespread applications
in engineering optimization problems, where the objective functions are often found to be …

Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …

Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …

Feature selection (FS) is the activity of defining the most contributing feature subset among
all used features to improve the superiority of datasets with a large number of dimensions by …

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The combination and …

The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) into the
market brings up many technical problems that need to be addressed. In the near future, a …

Most existing multiobjective evolutionary algorithms aim at approximating the Pareto front
(PF), which is the distribution of the Pareto-optimal solutions in the objective space. In many …

This article introduces the concept of stacking sequence table (SST) for the optimal design of
laminated composite structures with ply drops. The SST describes the sequence of ply-drops …

Obtaining an optimal solution for a permutation flowshop scheduling problem with the total
flowtime criterion in a reasonable computational timeframe using traditional approaches and …