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K satisfiability programming by using estimation of distribution algorithm in Hopfield neural network

Ahmad Rasli, Norul Fazira and Mohd. Kasihmuddin, Mohd. Shareduwan and Mansor, Mohd. Asyraf and Md. Basir, Md. Faisal and Sathasivam, Saratha (2020) K satisfiability programming by using estimation of distribution algorithm in Hopfield neural network. In: 27th National Symposium on Mathematical Sciences, SKSM 2019, 26 November 2019 - 27 November 2019, Tenera Hotel Bangi, Selangor, Malaysia.

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Official URL: http://dx.doi.org/10.1063/5.0018263

Abstract

Hopfield Neural Network (HNN) is a sort of neural network that is strongly dependent to energy minimization of solution. Although HNN managed to solve various optimization problem, the output of HNN suffered from a lack of interpretability and variation. This has severely limited the practical usability of HNN in doing logic programming. Inspired by random neuron perturbation, Estimation of Distribution Algorithm (EDA) has been proposed to explore various optimal neuron state. EDAs employs a probabilistic model to sample the neuron state in order to move toward the various optimal location of global minimum energy. In this paper, a new Mutation Hopfield Neural Network (MHNN) will be proposed to do k Satisfiability programming. Based on the experimental result, the proposed MHNN has outperformed conventional HNN in various performance metric.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:hopfield neural network, mutation, probabilistic model
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:90425
Deposited By: Yanti Mohd Shah
Deposited On:30 Apr 2021 14:54
Last Modified:30 Apr 2021 14:54

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