Real-world optimization problems typically involve multiple objectives to be optimized
simultaneously under multiple constraints and with respect to several variables. While multi …

A general review of game-theory based evolutionary algorithms (EAs) is presented in this
study. Nash equilibrium, Stackelberg game and Pareto optimality are considered, as game …

In many practical applications, the end-goal of multi-objective optimization is to select an
implementable solution that is close to the Pareto-optimal front while satisfying the decision …

In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in
the research community, motivated by the need for explanations in critical AI applications …

Many decision-making processes in our society involve NP-hard optimization problems. The
largescale, dynamism, and uncertainty of these problems constrain the potential use of …

Metaheuristic search algorithms look for solutions that either maximise or minimise a set of
objectives, such as cost or performance. However most real-world optimisation problems …

One of the crucial challenges of solving many-objective optimization problems is uniformly
well covering of the Pareto-front (PF). However, many the state-of-the-art optimization …

Automated calibration is a crucial stage when validating non-linear dynamic systems. The
modeler must control the calibration results and analyze parameter values in an iterative …

We use an expression for the error variance of geostatistical predictions, which includes the
effect of uncertainty in the spatial covariance parameters, to examine the performance of …

One of the main obstacles of adopting digital pathology is the challenge of efficient
processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs) …