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 A Comparison between Lamarckian Evolution and Behavior Evolution of Neural Network (Unpublished M.Sc.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
Statistics
Abstract views: 278PDF downloads: 15
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Amina KINANE DAOUADJI, Fatima BENDELLA, IMPROVING E-LEARNING BY FACIAL EXPRESSION ANALYSIS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Muayed S AL-HUSEINY, Ahmed S SAJIT, BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI , Applied Computer Science: Vol. 18 No. 1 (2022)
- Ghania ZIDANI, Djalal DJARAH, Abdslam BENMAKHLOUF, Laid KHETTACHE, OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Puja SARAF, Jayantrao PATIL, Rajnikant WAGH, ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION , Applied Computer Science: Vol. 20 No. 4 (2024)
- Sylwester KORGA, Kamil ŻYŁA, Jerzy JÓZWIK, Jarosław PYTKA, Kamil CYBUL, PREDICTIVE TOOLS AS PART OF DECISSION AIDING PROCESSES AT THE AIRPORT – THE CASE OF FACEBOOK PROPHET LIBRARY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Elmehdi BENMALEK, Jamal EL MHAMDI, Abdelilah JILBAB, Atman JBARI, A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Pascal Krutz, Matthias Rehm, Holger Schlegel, Martin Dix, RECOGNITION OF SPORTS EXERCISES USING INERTIAL SENSOR TECHNOLOGY , Applied Computer Science: Vol. 19 No. 1 (2023)
- Roman GALAGAN, Serhiy ANDREIEV, Nataliia STELMAKH, Yaroslava RAFALSKA, Andrii MOMOT, AUTOMATION OF POLYCYSTIC OVARY SYNDROME DIAGNOSTICS THROUGH MACHINE LEARNING ALGORITHMS IN ULTRASOUND IMAGING , Applied Computer Science: Vol. 20 No. 2 (2024)
- Thanh-Nghia NGUYEN, Thanh-Hai NGUYEN, Ba-Viet NGO, R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD , Applied Computer Science: Vol. 18 No. 3 (2022)
- Marcin Topczak, Małgorzata Śliwa, ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY , Applied Computer Science: Vol. 17 No. 1 (2021)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.