GENETIC ALGORITHMS - A POWERFUL TOOL FOR LARGE-SCALE NONLINEAR OPTIMIZATION PROBLEMS

Genetic algorithms represent an efficient global method for nonlinear optimization problems, that are encountered in the earth sciences. They share the favorable characteristics of random Monte Carlo over local optimization methods in that they do not require linearizing assumptions nor the calculation of partial derivatives, are independent of the misfit criterion, and avoid numerical instabilities associated with matrix inversion. The additional advantages over conventional methods such as iterative least squares is that the sampling is global, rather than local, thereby reducing the tendency to become entrapped in local minima and avoiding a dependency on an assumed starting model. In contrast to random Monte Carlo, however, they also share a desirable characteristic of the local methods in that they assimilate and take advantage of information collected during the sampling of the model space, resulting in an extremely efficient and robust optimization technique. This paper describes the basic genetic algorithm, briefly highlights some recent applications in the earth sciences and concludes that, in this field, the methodology should have many applications.