To approximate the Pareto front, most existing multiobjective evolutionary algorithms store
the nondominated solutions found so far in the population or in an external archive during …

This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based
on joint probabilistic modeling of objectives and variables. This EDA uses the …

Inverse model based multiobjective evolutionary algorithm aims to sample candidate
solutions directly in the objective space, which makes it easier to control the diversity of non …

Abstract Many-Objective Optimization Problems, or MaOPs, are complex optimization
problems with more than three objective functions. Traditional Multi-Objective Evolutionary …

This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach
to recover the Pareto front of a multiobjective optimization problem. ALP casts the …

Large optimization problems that involve either a large number of decision variables or
many objectives pose great challenges to nature inspired optimization algorithms. On the …

The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves
about the idea of exploiting the problem structure to implement more efficient evolutionary …

Particle filters, also known as sequential Monte Carlo (SMC) methods, constitute a class of
importance sampling and resampling techniques designed to use simulations to perform on …

As one of the most competitive approaches to multi-objective optimization, evolutionary
algorithms have been shown to obtain very good results for many real-world multi-objective …

The objective values information can be incorporated into the evolutionary algorithms based
on probabilistic modeling in order to capture the relationships between objectives and …