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 i s not at all a straightforward matter for many potential practitioners , among which are computational chemists. Both parts of this paper pro vide keys to this group of scientists that should enable them to open gates towards genetic algorithm applications on computers. After a gen eral introduction, Part I presents a taxonomy for genetic algorithm so ftware. The Discussion highlights important properties of different ki nds of genetic algorithm software, and proposes a strategy to the appl ied scientist who needs an executable application without first having to become an expert in genetic algorithm science. The material presen ted is largely based on our past experience, which includes the insigh ts that we gained during the development and use of our software libra ry GATES. Applications built with GATES are spread across various fiel ds of computational chemistry. GATES is described in Part II, the acco mpanying paper.