Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

Nature has always been a great source of inspiration for the development of computational approaches for optimization. Two major groups representing this class of biologically inspired algorithms are Swarm Intelligence and Evolutionary Computation. Such algorithms are called metaheuristics and are recognized to be efficient approaches for solving complex problems. Both Swarm Intelligence and Evolutionary Computation share common features such as the use of stochastic components during the optimization process and various parameters for configuration. The setup of parameters of an algorithm has an important role in defining its behavior, guiding the search and biasing the quality of the solutions found. However, adjusting the parameters is not a simple task, becoming an optimization problem within the problem being optimized. In addition, an appropriate setting for the parameters may change during the optimization process making this task even harder. There are two ways to adjust the parameters of an algorithm. The offline control that is performed before running the algorithm and parameter values remains fixed and the online control where parameter values may change during the optimization process. This article focuses on reviewing the online parameter control strategies applied in Evolutionary Computation and Swarm Intelligence. As a result, this review analyzes and points out the key techniques and algorithms used and suggests some directions for future research. Key-words: Parameter Setup, Evolutionary Computation, Swarm Intelligence, Online Control