This article provides an overview of evolutionary robotics techniques applied to online
distributed evolution for robot collectives, namely, embodied evolution. It provides a …

This paper contains a modern vision of the parallelization techniques used for evolutionary
algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of …

Recently, several meta-heuristics and evolutionary algorithms have been proposed for
tackling optimization problems. Such methods tend to suffer from degraded performance …

Abstract Dynamic Multi-objective Optimization is a challenging research topic since the
objective functions, constraints, and problem parameters may change over time. Although …

Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the
social behavior of ant colonies for solving optimization problems. When facing large and …

This book is the result of several years of research trying to better characterize parallel
genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers …

The field of evolutionary computation (EC) can no longer be considered an esoteric one.
Today, after about thirty years of research, a rich corpus of theory exists and many …

The purpose of this paper is to propose effective parallelization strategies for the Ant Colony
Optimization (ACO) metaheuristic on Graphics Processing Units (GPUs). The Max–Min Ant …

This authoritative handbook reveals the connections between bioinspired techniques and
the development of solutions to problems that arise in diverse problem domains. It provides …

In this work, we explore a novel multi-objective optimization algorithm to identify a set of
solutions that could be optimal for more than one task. The proposed approach is used to …