USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS
Wafaa Mustafa HAMEED
wafaa.mustafa@sulicihan.edu.krdAssistant lecturer, Department of Computer Science, Cihan University – Slemani, Slemani (Iraq)
Asan Baker KANBAR
Assistant lecturer, Department of Computer Science, Cihan University – Slemani, Slemani (Iraq)
Abstract
This article aims at studying the behavior of different types of crossover operators in the performance of Genetic Algorithm. We have also studied the effects of the parameters and variables (crossover probability (pc), mutation probability (pm), population size (pop-size) and number of generation (NG)) for controlling the algorithm. This research accumulated most of the types of crossover operators these types are implemented on evolving weights of Neural Network problem. The article investigates the role of crossover in GAs with respect to this problem, by using a comparative study between the iteration results obtained from changing the parameters values (crossover probability, mutation rate, population size and number of generation). From the experimental results, the best parameters values for the Evolving Weights of XOR-NN problem are NG = 1000, pop-size = 50, pm = 0.001, pc = 0.5 and the best operator is Line Recombination crossover.
Keywords:
genetic algorithm, neural network, crossover, mutationReferences
Al-Inazy, Q. A. (2005). A Comparison between Lamarckian Evolution and Behavior Evolution of Neural Network (Unpublished M.Sc. Thesis). Al- Mustansriyah University, Baghdad, Iraq.
Google Scholar
Arjona, D. (1996). A hybrid artificial neural network/genetic algorithm approach to on-line operations for the optimization of electrical power systems. In IECEC 96. Proceedings of the 31st Intersociety Energy Conversion Engineering Conference (pp. 2286–2290 vol. 4). Washington, DC, USA. https://doi.org/10.1109/IECEC.1996.561174
DOI: https://doi.org/10.1109/IECEC.1996.561174
Google Scholar
Goldberg, D. E. (1989). Genetic Algorithms in search, Optimization, and Machine Learning. Boston, MA, USA: Addison–Wesley Longman Publishing Co., Inc.
Google Scholar
Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA, USA: MIT Press.
Google Scholar
Michalewicz, Z. (1996). Genetic Algorithm + Data Structure = Evolution Programs, 3rd Revised Extended Edition. New York, USA: Springer – Verlag Berlin Heidelberg.
DOI: https://doi.org/10.1007/978-3-662-03315-9
Google Scholar
Mitchell, M. (1998). An Introduction of Genetic Algorithms. Cambridge, MA, USA: MIT Press.
DOI: https://doi.org/10.7551/mitpress/3927.001.0001
Google Scholar
Montana, D., & Davis, L. (1989). Training Feed Forward neural networks using Genetic Algorithms, In IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence (pp. 762–767). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
Google Scholar
Hameed, W. M., & Kanbar, A. B. (2017). A Comparative Study of Crossover Operators for Genetic Algorithms to Solve Travelling Salesman Problem. International Journal of Research – Granthaalayah, 5(2), 284–291. https://doi.org/10.5281/zenodo.345734
DOI: https://doi.org/10.29121/granthaalayah.v5.i2.2017.1740
Google Scholar
Hameed, W. M. (2016). The Role of Crossover on Optimization of a Function Problem Using Genetic Algorithms. International Journal of Computer Science and Mobile Computing, 5(7), 425–429.
Google Scholar
Weisman, O., & Pollack, Z. (2002). Neural Networks Using Genetic Algorithm. Retrieved from http://www.cs.bgu.ac.il/NNUGA.
Google Scholar
Whitley, D., Starkweather, T., & Fuquay, D. A. (1989). Scheduling Problems and Traveling Salesman: The Genetic Edge Recombination Operator. ICGA.
Google Scholar
Whitley, D. (1995). Genetic Algorithms and Neural Networks. In J. Periaux & G. Winter (Eds.), Genetic Algorithms in Engineering and Computer Science (pp. 191-201). John Wiley & Son Corp.
Google Scholar
Wright, A. H. (1991). Genetic Algorithms for Real Parameters Optimization. Foundation of Genetic Algorithms, 1, 205-218. https://doi.org/10.1016/B978-0-08-050684-5.50016-1
DOI: https://doi.org/10.1016/B978-0-08-050684-5.50016-1
Google Scholar
Authors
Wafaa Mustafa HAMEEDwafaa.mustafa@sulicihan.edu.krd
Assistant lecturer, Department of Computer Science, Cihan University – Slemani, Slemani Iraq
Authors
Asan Baker KANBARAssistant lecturer, Department of Computer Science, Cihan University – Slemani, Slemani Iraq
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