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Systems research, genetic algorithms and information systems
Author(s) -
Chaudhry Sohail S.,
Varano Michael W.,
Xu Lida
Publication year - 2000
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/(sici)1099-1743(200003/04)17:2<149::aid-sres290>3.0.co;2-q
Subject(s) - computer science , information system , management science , data science , algorithm , computational biology , cognitive science , artificial intelligence , psychology , biology , engineering , electrical engineering
Darwinian evolution and genetics have spawned a class of computational methods 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 the proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and evolutionary algorithms is systems science. Recent changes in technology, coupled 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, which is integrated into the information systems of the organization. A portfolio of examples illustrating the evolving and expanding applications of genetic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright © 2000 John Wiley & Sons, Ltd.