Modified Artificial Immune System Algorithm with Elliot Hopfield Neural Network For 3-Satisfiability Programming

Mohd Asyraf Mansor, Mohd. Shareduwan Mohd. Kasihmuddin, Saratha Sathasivam


The modified artificial immune system algorithm, hybridized with the Elliot Hopfield Neural Network, is proposed in doing the 3-Satisfiability programming (EHNN-3SATAIS). The main impetus of this paper is to investigate the effectiveness and capability of the proposed approach in the 3-Satisfiability programming with different complexities based on the number of neurons. The performance analysis of the proposed technique is assessed by integrating the comprehensive simulation via Dev C++ Version 5.11 and compared with the state-of-the-art Elliot Hopfield networks. The Elliot Hopfield network is to be incorporated into the exhaustive search (EHNN-3SATES) and genetic algorithm (EHNN-3SATGA). The simulated results depicted the performance of the paradigms in terms of mean absolute error (MAE), Schwarz Bayesian Criterion (SBC) and CPU Time. The proposed approach, EHNN-3SATAIS outperformed its other two conventional counterparts in term of accuracy, sensitivity, and robustness during the simulation. Hence, the simulation has proven that the modified artificial immune system algorithm complied effectively in tandem with the Elliot Hopfield neural network in doing the 3-Satisfiability logic programming.


Artificial immune system algorithm; Elliot Hopfield neural network; Elliot symmetric activation function; Satisfiability logic; Genetic algorithm

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