GATES TOWARDS EVOLUTIONARY LARGE-SCALE OPTIMIZATION - A SOFTWARE-ORIENTED APPROACH TO GENETIC ALGORITHMS .1. GENERAL PERSPECTIVE

Genetic algorithms comprise a novel methodology that has proven to be powerful in approaching complex, large-scale optimization problems in a wide variety of sciences, recently including computational chemistry. However, as it turns out, up to now the exploitation of this power is not at all a straightforward matter for many potential practitioners, among which are computational chemists. Both parts of this paper provide keys to this group of scientists that should enable them to open gates towards genetic algorithm applications on computers. After a general introduction, Part I presents a taxonomy for genetic algorithm software. The Discussion highlights important properties of different kinds of genetic algorithm software, and proposes a strategy to the applied scientist who needs an executable application without first having to become an expert in genetic algorithm science. The material presented is largely based on our past experience, which includes the insights that we gained during the development and use of our software library GATES. Applications built with GATES are spread across various fields of computational chemistry. GATES is described in Part II, the accompanying paper.