Application of an Evolution Program for Refrigerant Circuitry Optimization | NIST
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[1] Theodore V. Vorburger,et al. Project Report (1998-99) of NIST Standard Bullets and Casings (National Institute of Standards and Technology, Gaithersburg, MD) , 2000 .
[2] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[3] Robert W. Besant,et al. HVAC Duct System Design Using Genetic Algorithms , 2000 .
[4] A. C. West,et al. Optimization of multistage vapour compression systems using genetic algorithms. Part 2: Application of genetic algorithm and results , 2001 .
[5] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[6] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.
[7] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.
[8] G. K. Nathan,et al. Numerical and experimental studies of refrigerant circuitry of evaporator coils , 2001 .
[9] J. M. Calm,et al. Refrigerant Data Summary , 2001 .
[10] P. Domanski,et al. Performance of a finned-tube evaporator optimized for different refrigerants and its effect on system efficiency * , 2005 .
[11] Ryszard S. Michalski,et al. LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning , 2004, Machine Learning.
[12] David A. Yashar,et al. Optimization of finned-tube condensers using an intelligent system * , 2007 .
[13] Gregory Nellis,et al. Elevated-pressure mixed-coolants Joule-Thomson cryocooling , 2006 .