Abstract
Evolutionary algorithms (EAs) are a class of stochastic search algorithms which are 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 the discussion 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.
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