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

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.