Genetic and Evolutionary Algorithms

Many applications in computational chemistry have a search space that is exponentially proportional to the problem dimensions and but for the simplest of cases these problems cannot be solved using exhaustive search methods. Consequently, there is considerable interest in heuristic techniques that attempt to discover near-optimal solutions within an acceptable time. Genetic algorithms (GAs) and other related evolutionary algorithms (EAs) provide a framework for effectively sampling large search spaces, and the basic technique is both broadly applicable and easily tailored to specific problems (see Genetic Algorithms: Introduction and Applications). All that is required to apply an EA to any particular problem is an appropriate encoding scheme and a target function. Since 1992 we have seen an explosion in the number of seemingly intractable problems to which EAs have been successfully applied. This article reviews the application of EAs within the field of computational chemistry.