Using cultural algorithms in industry
Nestor Rychtyckyj, David Alfred Ostrowski, George Schleis, Robert G. Reynolds · 2004
Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we discuss the use of a specific form of evolutionary computation known as cultural algorithms that has been applied successfully in various real-world applications to solve problems of a very dynamic and complex nature. Cultural algorithms introduce a learning component into an evolutionary framework that influences the search strategy and is in turn modified by the best-performing members of the population during the entire process. Cultural algorithms have been used in various applications, including fraud analysis for automotive accident claims, the re-engineering of a dynamic automobile manufacturing knowledge base, the modeling of pricing strategies for automobiles in a multi-agent environment and for data mining.