We consider and discuss the ways in which search landscapes might contribute to the future
of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to …

Segmentation is an important task in image processing because it could affect the
performance of other steps in image analysis. One of the most used methods for …

This work investigates different Bayesian network structure learning techniques by
thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of …

While multi-joint continuum robots are highly dexterous and flexible, designing an optimal
robot can be challenging due to its kinematics involving curvatures. Hence, the current work …

Substitution Boxes (S-boxes) are nonlinear objects often used in the design of cryptographic
algorithms. The design of high quality S-boxes is an interesting problem that attracts a lot of …

During the evolutionary process, algorithms based on probability distributions for generating
new individuals suffer from computational burden due to the intensive computation of …

Problems with multiple interdependent components offer a better representation of the real-
world situations where globally optimal solutions are preferred over optimal solutions for the …

The Bayesian Optimisation Algorithm (BOA) is an Estimation of Distribution Algorithm (EDA)
that uses a Bayesian network as probabilistic graphical model (PGM). During the …

A configuration design problem in mechanical engineering involves finding an optimal
assembly of components and joints that realizes some desired performance criteria. Such a …

Abstract Dynamic Multiobjective Problems cover a set of real-world problems that have
many conflicting objectives. These problems are challenging and well known by the …