An introduction on global optimization by genetic algorithms

Abstract

The purpose of this paper is to influence the researchers working in the field of algorithms for large scale systems about the efficiency of evolution algorithms as optimization techniques to explore robust large search spaces and find near-global optima. These evolutionary algorithms (EAs) can be an alternative to numerical methods in difficult optimization problems like complex systems where the phenomena are difficult to model due to uncertainy, noise or even too little knowledge of the real problem. In such cases EAs are robust procedures to overcome these difficulties. In general these algorithms are not just an alternative to traditional methods, nowadays they are used hybridised form to complement and extend numerical methods. The convergence of heuristic optimization techniques is not affected by the continuity or differentiabililty of the functions to be optimized in the applications. These algorithms only require evaluation of the function in search space points. Applications of these evolutionary algorithms have been more convincing than, their theory, which is still weak, though under progress. This paper is divided in two parts. A large number of references are included to enhance the presentation of the material. Finally, we describe the main aspects for solving an optimization problem of interest in Aerospace Industry by Genetic Algorithms. The problem considered is the op timum design of an airfoil shape, which is an inverse problem that consists of finding the shape for a given pressure distribution on the airfoil.