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

Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for
solving multiobjective optimization problems. Especially more recent multiobjective …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

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

The goal of this book is to develop efficient optimization algorithms to solve diverse real-
world problems of graded difficulty. Genetic and evolutionary mechanisms have been …

In modern science and engineering, laboratory experiments are replaced by high fidelity
and computationally expensive simulations. Using such simulations reduces costs and …

This chapter aims to address some of the fundamental issues that are often encountered in
optimization problems, making them difficult to solve. These issues include premature …

Distributed particle filters (PFs) have received extensive attention because of its excellent
performance. Generally, it needs to transmit particles to multiple processing units (PUs) in …

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