ABSTRACT
Genetic algorithm (GA) is a numerical optimization algorithm that is capable of being applied to a wide range of optimization problems, guaranteeing the survival of the fittest. The GA begins with a set of initial random populations represented in chromosomes; each one consists of some genes. This chapter provides an introduction on the GA mechanism, and the GA application for optimal tuning of supplementary frequency controllers. It presents automatic generation control (AGC) design as a multiobjective GA optimization problem, and addresses the GA-based AGC synthesis to achieve the same robust performance indices as provided by the standard mixed H2/H8 control theory. The majority of control design problems are inherently multiobjective problems, in that there are several conflicting design objectives that need to be simultaneously achieved in the presence of determined constraints. The chapter highlights the capability of GA to improve the learning performance in the AGC systems using a learning algorithm.
