Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers

International Journal of Intelligent Systems

Volumn 21, Issue 12, 2006, Pages 1217-1248

  Michalski, Ryszard S ^a,b   Kaufman, Kenneth A ^a  

^a GEORGE MASON UNIVERSITY   (United States)

^b INSTITUTE OF COMPUTER SCIENCE   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; COOLING SYSTEMS; ENGINEERING RESEARCH; EXPERT SYSTEMS; HEAT EXCHANGERS; MATHEMATICAL MODELS;

DARWINIAN EVOLUTIONARY COMPUTATION; ENGINEERING SYSTEMS; HUMAN EXPERTS; LEARNABLE EVOLUTION MODEL (LEM);

SYSTEMS ANALYSIS;

EID: 33751557788     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.20182     Document Type: Article

References (31)

1. Michalski R. Learnable evolution: Combining symbolic and evolutionary learning. Proc 4th Int Workshop on Multistrategy Learning; 1998. pp 14-20.
2. Michalski R. Learnable evolution model: Evolutionary processes guided by machine learning. Mach Learn 2000;38:9-40.
3. Kaufman K, Michalski R. Applying learnable evolution model to heat exchanger design. Proc 12th Int Conf on Innovative Applications of Artificial Intelligence; 2000. pp 1014-1019.
4. Wojtusiak J. The LEM3 implementation of learnable evolution model: User's guide. Reports of the Machine Learning and Inference Laboratory, MLI04-5, George Mason University, Fairfax, VA; 2004.
5. Wojtusiak J, Michalski R. The LEM3 system for non-Darwinian evolutionary computation and its application to complex function optimization. Reports of the Machine Learning and Inference Laboratory, MLI 05-2, George Mason University, Fairfax, VA; 2005.
6. Michalski R, Kaufman K. The AQ19 system for machine learning and pattern discovery: A general description and user's guide. Reports of the Machine Learning and Inference Laboratory, MLI 01-2, George Mason University, Fairfax, VA; 2001.
7. Oyama A. Multidisciplinary optimization of transonic wing design based on evolutionary algorithms coupled with CFD solver. In: European Congress on Computational Methods in Applied Sciences and Engineering; 2000 (CD-ROM).
8. Michalski R. Attributional calculus: A logic and representation language for natural induction. Reports of the Machine Learning and Inference Laboratory, MLI 04-2, George Mason University, Fairfax, VA; 2004.
9. Bentley P, editor. Evolutionary design by computers. Menlo Park, CA: Morgan Kaufmann; 1999.
10. Bentley P, Corne D. Creative evolutionary systems. San Francisco, CA: Morgan Kaufmann/Academic Press; 2002.
11. Siddall J. Optimal engineering design - Principles and applications. New York: Marcel Decker; 1982.
12. Michalewicz Z. Genetic algorithms + data structures = evolution programs, 3rd ed. London: Springer-Verlag; 1996.
13. Domanski P. EVSIM - An evaporator simulation model accounting for refrigerant and one dimensional air distribution. NISTIR 89-4133, National Institute of Standards and Technology, Gaithersburg, MD; 1989.
14. Michalski R. A theory and methodology of inductive learning. In Michalski R, Carbonell J, Mitchell T. editors. Machine learning: An artificial intelligence approach, Palo Alto: Tioga; 1983. pp 83-134.
15. Domanski P, Yashar D, Kaufman K, Michalski R. An optimized design of finned-tube evaporators using the learnable evolution model. Int J Heat Vent Air-Cond Refrigerat Res 2004;10:201-211.
16. Reynolds RG. An Introduction to Cultural Algorithms. In: Proc 3rd Annual Conf on Evolutionary Programming; 1994. pp 131-139.
17. Rychtyckyj N, Reynolds R. Using cultural algorithms to improve performance in semantic networks. In: Proc Congress on Evolutionary Computation; 1999. pp 1651-1663.
18. Saleem S, Reynolds R. Cultural algorithms in dynamic environments. In: Proc Congress on Evolutionary Computation; 2000. pp 1513-1520.
19. Bentley P. From coffee tables to hospitals: Generic evolutionary design. In: Bentley P, editor. Evolutionary design by computers. Menlo Park, CA: Morgan Kaufmann; 1999. pp 405-423.
20. Sebag M, Schoenauer M. Controlling crossover through inductive learning. In: Proc Third Conf on Parallel Problem Solving from Nature; 1994. pp 209-218.
21. Sebag M, Schoenauer M, Ravise C. Inductive learning of mutation step-size in evolutionary parameter optimization. In: Proc Sixth Annual Conf on Evolutionary Programming; 1997. pp 247-261.
22. Ravise C, Sebag M. An advanced evolution should not repeat its past errors. In: Proc 13th Int Conf on Machine Learning; 1996. pp 400-408.
23. Sebag M, Shoenauer M, Ravise C. Toward civilized evolution: Developing inhibitions. In: Proc Seventh Int Conf on Genetic Algorithms; 1997. pp 291-298.
24. Grefenstette J. Lamarckian learning in multi-agent environment. In: Proc Fourth Int Conf on Genetic Algorithms; 1991. pp 303-310.
25. Rasheed K. GADO: A genetic algorithm for continuous design optimization. Technical Report DCS-TR-352, Department of Computer Science, Rutgers University, New Brunswick, NJ; 1998.
26. Moscato P. Memetic algorithms: A short introduction. In: Come D, Dorigo M, Glover F, Dasgupta D, Moscato P, Poli R, Price KV, editors. New ideas in optimization. Maidenhead, England: McGraw Hill; 1999. pp 219-234.
27. Runggeratigul S. A memetic algorithm for communication network design taking into consideration an existing network. In: Resenda MGC, Sousa JP, editors. Metaheuristics: Computer decision-making. Boston: Kluwer Academic Publishers; 2004. pp 615-626.
28. Embrechts M, Ozdemir M, Lockwood L, Breneman C, Bennet K, Devogelaere D, Rijkaert M. Feature selection methods based on genetic algorithms for in silico drug design. In: Fogel G, Corne D, editors. Evolutionary computation in bioinformatics. San Francisco: Morgan Kaufmann; 2002. pp 317-340.
29. de Garis H, Korkin M. The cam-brain machine (CBM): An FPGA based hardware tool which evolves a 1000 neuron net circuit module in seconds and updates a 75 million neuron artificial brain for real time robot control. Neurocomputing 2002;42:35-68.
30. Coletti M, Lash T, Mandsager C, Michalski R, Moustafa R. An experimental application of the learnable evolution model system LEM1 and genetic algorithms GA1 and GA2 to parameter identification in digital signal filter design. Reports of the Machine Learning and Inference Laboratory, MLI 99-5, George Mason University, Fairfax, VA; 1999.
31. Ashley J. Experiments with LEM3: Insights into generator electricity market competition between two heterogeneous agents using a game theoretic approach. Reports of the Machine Learning and Inference Laboratory, George Mason University, 2006 (in press).