ABSTRACT
In many engineering disciplines a large spectrum of optimization problems has grown in size and complexity. In some instances, the solution to complex multidimensional problems by using classical optimization techniques is sometimes difficult or expensive. This realization has led to an increased interest in a special class of searching algorithm, namely, evolutionary algorithms. In this area of operational research, there exist several primary branches: genetic algorithms (GAs), evolutionary programming, and evolutionary strategies. To understand the roots of GAs, the chapter looks at the biological analogy. In biological organisms, a chromosome carries a unique set of information that encodes the data on how the organism is constructed. GAs have been successfully used in market forecasting with well-known systems such as the prediction and state estimation applications. Evolutionary algorithms represent a broad class of computer-based problem-solving systems. Their key feature is the evolutionary mechanisms that are at the root of formulation and implementation.
