Systems research, genetic algorithms and information systems

Darwinian evolution and genetics have spawned a class of computational meth ods called evolutionary algorithms, and in particular, genetic algorithms. These evolutionary strategies provide new opportunities and challenges with ever-increasing applications in industry. In this paper, we propose that t he proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and ev olutionary algorithms is systems science. Recent changes in technology, cou pled with developments in the field of artificial intelligence, promote the growth of enabling technologies, such as intelligent systems, in which we integrate genetic algorithms. Genetic algorithms are integrated with other artificial intelligence tools using a cooperating intelligent subsystem, wh ich is integrated into the information systems of the organization. A portf olio of examples illustrating the evolving and expanding applications of ge netic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright (C) 20 00 John Wiley & Sons, Ltd.