USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS

Wafaa Mustafa HAMEED; Asan Baker KANBAR

doi:10.23743/acs-2019-18

Open full text

pdf

Published: Sep 30, 2019

DOI: https://doi.org/10.23743/acs-2019-18

Issue Vol. 15 No. 3 (2019)

Articles

UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH
Grzegorz RADZKI, Amila THIBBOTUWAWA, Grzegorz BOCEWICZ

5-20
USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS
Wafaa Mustafa HAMEED, Asan Baker KANBAR

21-33
DIAGNOSTIC FACTORS FOR OPENED AND CLOSED KINEMATIC CHAIN OF VIBROARTHROGRAPHY SIGNALS
Anna MACHROWSKA, Robert KARPIŃSKI, Przemysław KRAKOWSKI, Józef JONAK

34-44
FUZZY ASSESSMENT OF MANUFACTURABILITY DESIGN FOR MACHINING
Józef MATUSZEK, Tomasz SENETA, Aleksander MOCZAŁA

45-55
THE APPLICATION OF FINGERPRINTS AUTHENTICATION IN DISTANCE EDUCATION
Hamid JAN, Beena HAMID

56-64
USEFULNESS OF RAPID PROTOTYPING IN PLANNING COMPLEX TRAUMA SURGERIES
Przemysław KRAKOWSKI, Józef JONAK, Robert KARPIŃSKI, Łukasz JAWORSKI

65-72
INEFFICIENCY OF DATA MINING ALGORITHMS AND ITS ARCHITECTURE: WITH EMPHASIS TO THE SHORTCOMING OF DATA MINING ALGORITHMS ON THE OUTPUT OF THE RESEARCHES
Workineh TESEMA

73-86
APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE
Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI

87-102

DOI

https://doi.org/10.23743/acs-2019-18

Authors

Wafaa Mustafa HAMEED

wafaa.mustafa@sulicihan.edu.krd

Assistant lecturer, Department of Computer Science, Cihan University – Slemani, Slemani, Iraq

Asan Baker KANBAR

asan.baker@sulicihan.edu.krd

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, mutation

References

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.

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

Goldberg, D. E. (1989). Genetic Algorithms in search, Optimization, and Machine Learning. Boston, MA, USA: Addison–Wesley Longman Publishing Co., Inc.

Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA, USA: MIT Press.

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

Mitchell, M. (1998). An Introduction of Genetic Algorithms. Cambridge, MA, USA: MIT Press. DOI: https://doi.org/10.7551/mitpress/3927.001.0001

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.

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

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.

Weisman, O., & Pollack, Z. (2002). Neural Networks Using Genetic Algorithm. Retrieved from http://www.cs.bgu.ac.il/NNUGA.

Whitley, D., Starkweather, T., & Fuquay, D. A. (1989). Scheduling Problems and Traveling Salesman: The Genetic Edge Recombination Operator. ICGA.

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.

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

HAMEED, W. M., & KANBAR, A. B. (2019). USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS. Applied Computer Science, 15(3), 21–33. https://doi.org/10.23743/acs-2019-18

USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS

Issue Vol. 15 No. 3 (2019)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 15 No. 3 (2019)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License