Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

Authors

DOI:

https://doi.org/10.9781/ijimai.2016.429

Keywords:

Genetic Algorithms, Neural Network, Hopfield, K-satisfiability
Supporting Agencies
The authors fully acknowledged Ministry of Higher Education (MOHE) and School of Mathematical Sciences, Universiti Sains Malaysia for the support which makes this important research viable and effective.

Abstract

The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem.

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Published

2016-12-01
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How to Cite

Bin Mohd Kasihmuddin, M. S., Bin Mansor, M. A., and Sathasivam, S. (2016). Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network. International Journal of Interactive Multimedia and Artificial Intelligence, 4(2), 52–60. https://doi.org/10.9781/ijimai.2016.429