Kaufman, Kenneth A.Michalski, Ryszard S.2006-11-032006-11-032000Kaufman, K. and Michalski, R. S., "Applying Learnable Evolution Model to Heat Exchanger Design," Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000) and Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2000), Austin, TX, pp. 1014-1019, 2000.https://hdl.handle.net/1920/1468This article copyright © 2000 by theA new approach to evolutionary computation, called Learnable Evolution Model (LEM), has been applied to the problem of optimizing tube structures of heat exchangers. In contrast to conventional Darwinian-type evolutionary computation algorithms that use various forms of mutation and/or recombination operators, LEM employs machine learning to guide the process of generating new individuals. A system, ISHED1, based on LEM, automatically searches for the highest capacity heat exchangers under given technical and environmental constraints. The results of experiments have been highly promising, often producing solutions exceeding the best human designs.1844 bytes393271 bytes146984 bytestext/xmlapplication/postscriptapplication/pdfen-USArticle