Global optimisation by evolutionary algorithms

Evolutionary algorithms (EAs) are a class of stochastic search algorithms applicable to a wide range of problems in learning and optimisation. They have been applied to numerous problems in combinatorial optimisation, function optimisation, artificial neural network learning, fuzzy logic system learning, etc. This paper first introduces EAs and their basic operators. Then an overview of three major branches of EAs, i.e., genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs) is given. Different search operators and selection mechanisms are described. The emphasis of all the discussions is on global optimisation by EAs. The paper also presents three simple models for parallel EAs. Finally, some open issues and future research directions in evolutionary optimisation and evolutionary computation in general are discussed.