Abstract
Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this chapter, we propose to implement a parallel EA on consumer-level Graphics Processing Unit (GPU). We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics processing units are already widely available and installed on oridinary personal computers and they are easy to use and manage, more people will be able to use our parallel algorithm to solve their problems encountered in real-world applications.
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References
P. Angeline, “Genetic programming and emergent intelligent,” in Advances in Genetic Programming, Jr K. E. Kinnear, Ed., pp. 75–97. MIT Press, Cambridge, MA, 1994.
Il-Seok Oh, Jin-Seon Lee, and Byung-Ro Moon, “Hybrid genetic algorithms for feature selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 142l4–1437, 2004.
John R. Koza, M. A. Keane, M. J. Streeter, W. Mydlowec, J. Yu, and G. Lanza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Kluwer Academic Publishers, 2003.
M. L. Wong, W. Lam, and K. S. Leung, “Using evolutionary programming and minimum description length principle forl data mining of Bayesian networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 2, pp. 174–178, February 1999.
M. L. Wong, W. Lam, K. S. Leung, P. S. Ngan, and J. C. Y. Cheng, “Discovering knowledge from medical databases using evolutionary algorithms,” IEEE Engineering in Medicine and Biology Magazine, vol. 19, no. 4, pp. 45–55, 2000.
Man Leung Wong and Kwong Sak Leung, “An efficient data mining method for learning Bayesian networks using an evolutionary algorithm based hybrid approach,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 378–104, 2004.
Alex A. Preitas, Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer, 2002.
J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
David E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
John R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, MIT Press, 1992.
Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, and Frank D. Francone, Genetic Programming: An Introduction, Morgan Kaufmann, San Francisco, California, 1998.
David B. Fogel, Evolutionary Computation: Toward a New Philosohpy of Machine Intelligence, IEEE Press, 2000.
L. Fogel, A. Owens, and M. Walsh, Artificial Intelligence Through Simulated Evolution, John Wiley and Sons, 1966.
H. P. Schewefel, Numerical Optimization of Computer Models, John Wiley and sons, New York, 1981.
Thomas Bäck, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Agorithms, Oxford University Press, 1996.
Erick Cantú-Paz, Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, 2000.
X. Yao and Y. Liu, “Fast evolutionary programming,” in Evolutionary Programming V: Processdings of the 5th Annual Conference on Evolutionary Programming. 1996, Cambridge, MA:MIT Press.
David B. Fogel, “An introduction to simulated evolutionary optimization,” IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 3–14, 1994.
G. E. P. Box and M. E. Muller, “A note on the generation of random normal deviates,” Annals of Mathematical Statistics, vol. 29, pp. 610–611, 1958.
D. E. Knuth, “The art of computer programming. volume 2: Seminumerial algorithms (second edition),” Addison-Wesley, Menlo Park, 1981.
Robert W. Floyd and Ronald L. Rivest, “Expected time bounds for selection,” Communications of the ACM, vol. 18(3), pp. 165–172, 1975.
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Wong, TT., Wong, M.L. (2006). Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit. In: Nedjah, N., Mourelle, L.d., Alba, E. (eds) Parallel Evolutionary Computations. Studies in Computational Intelligence, vol 22. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-32839-4_7
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DOI: https://doi.org/10.1007/3-540-32839-4_7
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