ABSTRACT
Genetic Algorithm (GA) is to convert the problem-solving process into a process similar to the chromosomal changes in biological evolution using the mathematical method and computer simulation operation. This meta-heuristic algorithm has been successfully applied to system logic and non-system logic programming. In this study, we will explore the role of the Bipolar Genetic Algorithm (GA) in enhancing the learning process of the Hopfield neural network based on the previous study of PRO2SAT, and generate global solutions of the Probabilistic 2 Satisfiability model. The main purpose of the learning phase of the PRO2SAT model is to obtain consistent interpretations and calculate the optimal prominence weights, and the GA algorithm is introduced to improve the ability of PRO2SAT to obtain consistent interpretation using its selection, crossover, and mutation operators, and thus to improve the ability of the logic programming model to get a global solution. In the experimental phase, simulation data are used for result testing, and three performance metrics are used to test the consistency interpretation and global solution acquisition ability of the proposed model, including mean absolute error, logic formula satisfaction ratio, and global minimum ratio. Experimental results show that GA, as a meta-heuristic algorithm, has better searching ability for optimal solution and can effectively assist logic programming.
- Beam, A. L.; Kohane, I. S. Big Data and Machine Learning in Health Care. JAMA 2018, 319, 1317-1318. https://doi:10.1001/jama.2017.18391Google ScholarCross Ref
- Vigilante, K.; Escaravage, S.; Mc Connell, M. Big Data and the Intelligence Community-Lessons for Health Care . N Engl J Med 2019, 380, 1888-1890. https://doi: 10.1056/NEJMp1815418Google ScholarCross Ref
- Abdullah, W. A. T. W. 1992. Logic programming on a neural network. International journal of intelligent systems, 7(6), 513-519. https://doi.org/10.1002/int.4550070604Google ScholarCross Ref
- Sathasivam, S. “Upgrading logic programming in Hopfifield network,” Sains Malaysiana 2010, 39, 115–118.Google Scholar
- Kasihmuddin, M. S. M., Mansor, M. A., & Sathasivam, S. (2017). Hybrid Genetic Algorithm in the Hopfield Network for Logic Satisfiability Problem. Pertanika Journal of Science & Technology, 25(1).Google Scholar
- Mohd Kasihmuddin, Mohd Shareduwan, Mohd. Asyraf Mansor, Md Faisal Md Basir, and Saratha Sathasivam. 2019. "Discrete Mutation Hopfield Neural Network in Propositional Satisfiability" Mathematics 7, no. 11: 1133. https://doi.org/10.3390/math7111133Google ScholarCross Ref
- Kasihmuddin, Mohd Shareduwan Mohd, Siti Zulaikha Mohd Jamaludin, Mohd. Asyraf Mansor, Habibah A. Wahab, and Siti Maisharah Sheikh Ghadzi. 2022. "Supervised Learning Perspective in Logic Mining" Mathematics 10, no. 6: 915. https://doi.org/10.3390/ math10060915Google ScholarCross Ref
- Mansor,M. A.; Sathasivam, S. Accelerating activation function for 3-satisfiability logic programming. International Journal of Intelligent Systems and Applications 2016, 8, 44-50. https://doi.org/10.5815/ijisa.2016.10.05Google ScholarCross Ref
- Chen, Ju, "PRO2SAT: Systematic Probabilistic Satisfiability logic in Discrete Hopfield Neural Network." Advances in Engineering Software 175 (2023): 103355. https://doi.org/10.1016/j.advengsoft.2022.103355Google ScholarDigital Library
- Gao, Yuan, Yueling Guo, Nurul Atiqah Romli, Mohd Shareduwan Mohd Kasihmuddin, Weixiang Chen, Mohd. Asyraf Mansor, and Ju Chen. 2022. "GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network" Mathematics 10, no. 11: 1899. https://doi.org/10.3390/math10111899Google ScholarCross Ref
- Karim, S. A., Zamri, N. E., Alway, A., Kasihmuddin, M. S. M., Ismail, A. I. M., Mansor, M. A., Hassan, N. F. A. 2021. Random satisfiability: A higher-order logical approach in discrete Hopfield Neural Network. IEEE Access, 9, 50831-50845. https://doi.org/10.1109/ACCESS.2021.3068998Google ScholarCross Ref
- Guo, Y., Kasihmuddin, M. S. M., Gao, Y., Mansor, M. A., Wahab, H. A., Zamri, N. E., & Chen, J. (2022). YRAN2SAT: A novel flexible random satisfiability logical rule in discrete hopfield neural network. Advances in Engineering Software, 171, 103169. https://doi.org/10.1016/j.advengsoft.2022.103169Google ScholarDigital Library
- Sathasivam, S., Mansor, M. A., Ismail, A. I. M., Jamaludin, S. Z. M., Kasihmuddin, M. S. M., & Mamat, M. (2020). Novel Random k Satisfiability for k≤ 2 in Hopfield Neural Network. Sains Malays, 49, 2847-2857. http://dx.doi.org/10.17576/jsm-2020-4911-23Google ScholarCross Ref
- Zamri, N. E., Azhar, S. A., Mansor, M. A., Alway, A., & Kasihmuddin, M. S. M. (2022). Weighted Random k Satisfiability for k= 1, 2 (r2SAT) in Discrete Hopfield Neural Network. Applied Soft Computing, 109312. https://doi.org/10.1016/j.asoc.2022.109312Google ScholarDigital Library
- Alway, A., Zamri, N. E., Karim, S. A., Mansor, M. A., Mohd Kasihmuddin, M. S., & Mohammed Bazuhair, M. (2022). Major 2 satisfiability logic in discrete Hopfield neural network. International Journal of Computer Mathematics, 99(5), 924-948. https://doi.org/10.1080/ 00207160.2021.1939870Google ScholarCross Ref
- Zamri, Nur Ezlin, "Multi-discrete genetic algorithm in hopfield neural network with weighted random k satisfiability." Neural Computing and Applications 34.21 (2022): 19283-19311. https://doi.org/10.1007/s00521-022-07541-6Google ScholarDigital Library
- Karim, Syed Anayet, Mohd Shareduwan Mohd Kasihmuddin, Saratha Sathasivam, Mohd. Asyraf Mansor, Siti Zulaikha Mohd Jamaludin, and Md Rabiol Amin. 2022. "A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network" Mathematics 10, no. 12: 1963. https://doi.org/10.3390/math10121963Google ScholarCross Ref
- Muhammad Sidik, Siti Syatirah, Nur Ezlin Zamri, Mohd Shareduwan Mohd Kasihmuddin, Habibah A. Wahab, Yueling Guo, and Mohd. Asyraf Mansor. 2022. "Non-Systematic Weighted Satisfiability in Discrete Hopfield Neural Network Using Binary Artificial Bee Colony Optimization" Mathematics 10, no. 7: 1129. https://doi.org/10.3390/math10071129Google ScholarCross Ref
Index Terms
- Genetic algorithm in hopfield neural network with probabilistic 2 satisfiability
Recommendations
An optimizing BP neural network algorithm based on genetic algorithm
A back-propagation (BP) neural network has good self-learning, self-adapting and generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of convergence. Therefore, a method to optimize a BP algorithm based on a ...
Neural network crossover in genetic algorithms using genetic programming
AbstractThe use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...
Structured genetic algorithm representations for neural network evolution
AIAP'07: Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applicationsEvolutionary Algorithms used to generate Artificial Neural Networks have relied on both binary and real value representation approaches to encode connection weights in the chromosomes. This paper documents a study which examined how the utilisation of ...
Comments