Abstract
Evolutionary computation has enjoyed a tremendous growth in more than a decade in both its theoretical foundations and industrial applications. Its scope has gone beyond its earlier meaning of “genetic evolution”. Many research topics in evolutionary computation nowadays are not necessarily “evolutionary” in any sense. There is a need for studying a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological computation, etc., in a unified framework This paper gives an overview of some work that has been going on in the Natural Computation Group at The University of Birmingham, UK. It covers topics in optimisation, learning and design using nature inspired algorithms and techniques. Some recent theoretical results in the computational time complexity of evolutionary and neural optimisation algorithms will also be mentioned.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
X. Yao, “An overview of evolutionary computation,” Chinese Journal of Advanced Software Research (Allerton Press, Inc., New York, NY 10011), vol. 3, no. 1, pp. 12–29, 1996.
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680,1983.
H. H. Szu and R. L. Hartley, “Fast simulated annealing,” Physics Letters A, vol. 122, pp. 157–162,1987.
L. Ingber, “Very fast simulated re-annealing,” Mathl. Comput. Modelling, vol. 12, no. 8, pp. 967–973, 1989.
X. Yao, “A new simulated annealing algorithm,” Int. J. of Computer Math., vol. 56, pp. 161–168, 1995.
D. B. Fogel, System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Needham Heights, MA 02194: Ginn Press, 1991.
D. B. Fogel, Evolving Artificial Intelligence. PhD thesis, University of California, San Diego, CA, 1992.
D. B. Fogel, “Applying evolutionary programming to selected traveling salesman problems,” Cybernetics and Systems, vol. 24, pp. 27–36, 1993.
X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, pp. 82–102, July 1999.
X. Yao and Y. Liu, “Fast evolution strategies,” Control and Cybernetics, vol. 26, no. 3, pp. 467–496, 1997.
R. A. Hunt, Calculus with Analytic Geometry. New York, NY 10022-5299: Harper & Row Publ., Inc., 1986.
T. P. Runarsson and X. Yao, “Stochastic ranking for constrained evolutionary optimization,” IEEE Transactions on Evolutionary Computation, vol. 4, pp. 284–294, September 2000.
X. Yao and Y. Liu, “Making use of population information in evolutionary artificial neural networks,” IEEE Trans, on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 28, no. 3, pp. 417–425,1998.
O. G. Selfridge, “Pandemonium: a paradigm for learning,” in Mechanisation of Thought Processes: Proc. of a Symp. Held at the National Physical Lab., pp. 513–526, HMSO, London, 1958.
L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans, on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993–1001, 1990.
A. Sharkey, “On combining artificial neural nets,” Connection Science, vol. 8, pp. 299–313, 1996.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural Computation, vol. 3, pp. 79–87, 1991.
R. A. Jacobs, M. I. Jordan, and A. G. Barto, “Task decomposition through competition in a modular connectionist architecture: the what and where vision task,” Cognitive Science, vol. 15, pp. 219–250,1991.
H. Drucker, C. Cortes, L. D. Jackel, Y. LeCun, and V. Vapnik, “Boosting and other ensemble methods,” Neural Computation, vol. 6, pp. 1289–1301, 1994.
Y. Liu and X. Yao, “Negatively correlated neural networks can produce best ensembles,” Australian Journal of Intelligent Information Processing Systems, vol. 4, no. 3/4, pp. 176–185,1997.
Y. Liu and X. Yao, “A cooperative ensemble learning system,” in 1998 IEEE International Joint Conference on Neural Networks, Anchorage, USA, (Piscataway, NJ, USA), pp. 2202–2207, IEEE Press, 1998.
Y. Liu and X. Yao, “Simultaneous training of negatively correlated neural networks in an ensemble,” IEEE Trans, on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 29, pp. 716–725, December 1999.
X. Yao and Y. Liu, “A new evolutionary system for evolving artificial neural networks,” IEEE Transactions on Neural Networks, vol. 8, no. 3, pp. 694–713,1997.
Y. Liu and X. Yao, “Towards designing neural network ensembles by evolution,” in Parallel Problem Solving from Nature (PPSN) V (A. E. Eiben, T. Back, M. Schoenauer, and H.-P. Schwefel, eds.), vol. 1498 of Lecture Notes in Computer Science, (Berlin), pp. 623–632, Springer-Verlag, 1998.
Y. Liu, X. Yao, and T. Higuchi, “Evolutionary ensembles with negative correlation learning,” IEEE Transactions on Evolutionary Computation, vol. 4, pp. 380–387, November 2000.
D. B. Fogel, Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. New York, NY: IEEE Press, 1995.
T. Schnier, X. Yao, and P. Liu, “Digital filter design using multiple pareto fronts,” in Proceedings of the Third NASA/DoD Workshop on Evolvable Hardware, pp. 136–145, IEEE Computer Society Press, CA, July 2001.
J. He and X. Yao, “Drift analysis and average time complexity of evolutionary algorithms,” Artificial Intelligence, vol. 127, pp. 57–85, March 2001.
X. Yao, ed., Evolutionary Computation: Theory and Applications. Singapore: World Scientific Publishing Co., 1999.
D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this paper
Cite this paper
Yao, X. (2002). From Evolutionary Computation to Natural Computation. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture V. Springer, London. https://doi.org/10.1007/978-0-85729-345-9_4
Download citation
DOI: https://doi.org/10.1007/978-0-85729-345-9_4
Publisher Name: Springer, London
Print ISBN: 978-1-85233-605-9
Online ISBN: 978-0-85729-345-9
eBook Packages: Springer Book Archive