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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/579105
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dc.contributor.authorMohd Shareduwan Mohd Kasihmuddin
dc.contributor.authorMohd Asyraf Mansor (USM)
dc.contributor.authorSaratha Sathasivam (USM)
dc.date.accessioned2023-11-06T03:14:49Z-
dc.date.available2023-11-06T03:14:49Z-
dc.date.issued2017-01
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116457
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/579105-
dc.descriptionIn this study, a hybrid approach that employs Hopfield neural network and a genetic algorithm in doing k-SAT problems was proposed. The Hopfield neural network was used to minimise logical inconsistency in interpreting logic clauses or programme. Hybrid optimisation made use of the global convergence advantage of the genetic algorithm to deal with learning complexity in the Hopfield network. The simulation incorporated with genetic algorithm and exhaustive search method with different k-Satisfiability (k-SAT) problems, namely, the Horn-Satisfiability (HORN-SAT), 2-Satisfiability (2-SAT) and 3-Satisfiability (3-SAT) will be developed by using Microsoft Visual C++ 2010 Express Software. The performance of both searching techniques was evaluated based on global minima ratio, hamming distance and computation time. Simulated results suggested that the genetic algorithm outperformed exhaustive search in doing k-SAT logic programming in the Hopfield network
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-1-1
dc.rightsUKM
dc.subjectGenetic Algorithm
dc.subjectExhaustive Search
dc.subjectHopfield network
dc.subjectSatisfiability
dc.subjectLogic Programming
dc.subjectHORN-SAT
dc.subject3-SAT
dc.subject2-SAT
dc.titleHybrid genetic algorithm in the hopfield network for logic satisfiability problem
dc.typeJournal Article
dc.format.volume25
dc.format.pages139-152
dc.format.issue1
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