During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

The automatic induction of machine learning models capable of addressing supervised
learning, feature selection, clustering and reinforcement learning problems requires …

Evolutionary Algorithms (EAs) has attracted significantly attention with respect to complexity
scheduling problems, which is referred to evolutionary scheduling. However, EAs differ in …

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox
for solving high-dimensional optimization problems in across a broad range of …

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

In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much
popularity in the evolutionary computation community for solving optimization problems …

In this paper we present a geometrical framework for the analysis of Estimation of
Distribution Algorithms (EDAs) based on the exponential family. From a theoretical point of …

Abstract This paper describes Mateda-2.0, a MATLAB package for estimation of distribution
algorithms (EDAs). This package can be used to solve single and multi-objective discrete …

Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that
can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are …

Soft robotic devices are designed for applications such as exploration, manipulation, search
and rescue, medical surgery, rehabilitation, and assistance. Due to their complex …