LAPSE:2020.0837
Published Article
LAPSE:2020.0837
Election Algorithm for Random k Satisfiability in the Hopfield Neural Network
July 17, 2020
Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.
Keywords
election algorithm, exhaustive search, Genetic Algorithm, Hopfield neural network, random k satisfiability
Suggested Citation
Sathasivam S, Mansor MA, Kasihmuddin MSM, Abubakar H. Election Algorithm for Random k Satisfiability in the Hopfield Neural Network. (2020). LAPSE:2020.0837
Author Affiliations
Sathasivam S: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
Mansor MA: School of Distance Education, Universiti Sains Malaysia, Penang 11800 USM, Malaysia [ORCID]
Kasihmuddin MSM: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia [ORCID]
Abubakar H: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia [ORCID]
Journal Name
Processes
Volume
8
Issue
5
Article Number
E568
Year
2020
Publication Date
2020-05-11
Published Version
ISSN
2227-9717
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Original Submission
Other Meta
PII: pr8050568, Publication Type: Journal Article
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LAPSE:2020.0837
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doi:10.3390/pr8050568
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Jul 17, 2020
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CC BY 4.0
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Jul 17, 2020
 
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Original Submitter
Calvin Tsay
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