Abstract
In this paper, we introduce an approach for solving a regression problem. In regression problems, one tries to reconstruct the original data from a noisy data set. We solve the problem using a genetic algorithm and a neural network called Multi Layer Perceptron (MLP) network. By constructing the neural network in an appropriate way, we are able to form an objective function for the regression problem. We solve the obtained optimization problem using a hybrid genetic algorithm and compare the results to those of a simple multistart method. The hybrid genetic algorithm used is a simple hybridization of a genetic algorithm and a Nelder-Mead simplex method.
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Bibliography
C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.
K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, 2001.
J. E. Dennis, Jr. and R. B. Schnabel. A view of unconstrained optimization. In Optimization, pages 1–72. Elsevier North-Holland, New York, 1989.
D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York, 1989.
H. Greenberg. Mathematical programming glossary. http://carbon.cudenver.edu/-hgreenberg/glossary/glossary.htm 2001.
M. Hagan and M. Mohammad. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5 (6): 989–993, 1994.
A. C. Harvey. The Econometric Analysis of Time Series. MIT Press, 2nd edition, 1990.
T. Kärkkäinen. MLP-network in a layer-wise form with applications to weight decay. Neural Computation, 14 (6): 1451–1480, 2002.
S. Kotz and N. L. Johnson, editors. Encyclopedia of Statistical Sciences, volume 3 and 7. John Wiley & Sons, New York, 1982.
J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright. Convergence properties of the Nelder—Mead simplex method in low dimensions. SIAM Journal on Optimization, 9 (1): 112–147, 1998.
H.-L. Liu. A hybrid AI optimization method applied to industrial processes. Chemometrics and Intelligent Laboratory Systems, 45 (1–2): 101–104, 1999.
Z. Michalewicz, T. D. Logan, and S. Swaminathan. Evolutionary operators for continuous convex parameter spaces. In A. V. Sebald and L. J. Fogel, editors, Proceedings of the 3rd Annual Conference on Evolutionary Programming, pages 84–97. World Scientific Publishing, River Edge, NJ, 1994.
Z. Michalewicz, G. Nazhiyath, and M. Michalewicz. A note on usefulness of geometrical crossover for numerical optimization problems. In L. J. L.J. Fogel, P. J. Angeline, and T. Back, editors, Proceedings of the 5th Annual Conference on Evolutionary Programming, pages 305–312. MIT Press, Cambridge, MA, 1996.
K. Miettinen, M. M. Mäkelä, and J. Toivanen. Comparison of four penalty function-based methods in handling constraints with genetic algorithms. Reports of the Department of Mathematical Information Technology, Series B. Scientific Computing No. B 17/2000, University of Jyväskylä, Department of Mathematical Information Technology, 2000.
D. T. Pham and D. Karaboga. Intelligent Optimization Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. Springer-Verlag, London, 2000.
R. Rojas. Neural Networks: A Systematic Introduction. Springer-Verlag, Berlin Heidelberg, 1996.
F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65 (6): 386–408, 1958.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representation by error propagation. In J. L. McClelland, D. E. Rumelhart, and the PDP Research Group, editors, Parallel Distributed Processing Explorations in the Microstructure of Cognition, volume 1: Foundations, pages 318–362. MIT Press, Cambridge, MA, 1996.
X. Yao and Y. Lui. New evolutionary systems for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8 (3): 694–713, 1997.
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Maaranen, H., Miettinen, K., Mäkelä, M.M. (2003). Training Multi Layer Perceptron Network Using a Genetic Algorithm as a Global Optimizer. In: Metaheuristics: Computer Decision-Making. Applied Optimization, vol 86. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-4137-7_20
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DOI: https://doi.org/10.1007/978-1-4757-4137-7_20
Publisher Name: Springer, Boston, MA
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