Mohd. Asyraf Mansor

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

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.

3-satisfiability logic programming approach for cardiovascular diseases diagnosis

3-Satisfiability logic programming is a brand-new approach in the data mining field. In recent years, the conventional data mining technique only emphasizes on the standalone neural network paradigm. To frame the novelty, the 3-Satisfiability logic programming is incorporated with the discrete Hopfield neural network as a single data mining tool. Hence, the proposed approach is applied in evaluating numerous cardiovascular diseases data sets. Pursuing that, the results obtained can assist the medical practitioners in cardiovascular disease early diagnosis. Dev C++ 5.11 was used as a platform for training, testing and validating the performances of the proposed approach. The performance of the proposed approach is evaluated by performance metrics such as root mean square error (RMSE), mean bias error (MBE), sum of squared error (SSE), symmetric mean absolute percentage error (SMAPE) and CPU time. The error evaluations and accuracy of the proposed method have demonstrated a promising result when applied in heart disease data set (HNN-3SATHDD) and statlog heart data (HNN-3SATSD). Therefore, the results had provided the concrete evidence of the effectiveness of the proposed approach in the data mining.

Artificial bee colony in neuro - Symbolic integration

Swarm intelligence is a research area that models the population of the swarm based on natural computation. Artificial bee colony (ABC) algorithm is a swarm based metaheuristic algorithm introduced by Karaboga to optimize numerical problem. Pattern-SAT is a pattern reconstruction paradigm that utilized 2SAT logical rule in representing the behavior of the desired pattern. The information of the desired pattern in terms of 2SAT logic is embedded to Hopfield neural network (HNN-P2SAT) and the desired pattern is reconstructed during the retrieval phase. Since the performance of HNN-P2SAT in Pattern-SAT deteriorates when the number of 2SAT clause increased, newly improved ABC is used to reduce the computation burden during the learning phase of HNN-P2SAT (HNN-P2SATABC). The aim of this study is to investigate the performance of Pattern-SAT produced by ABC incorporated with HNN-P2SAT and compare it with conventional standalone HNN. The comparison is examined by using Microsoft Visual Basic C++ 2013 software. T...

Post optimization paradigm in maximum 3-satisfiability logic programming

Maximum 3-Satisfiability (MAX-3SAT) is a counterpart of the Boolean satisfiability problem that can be treated as a constraint optimization problem. It deals with a conundrum of searching the maximum number of satisfied clauses in a particular 3-SAT formula. This paper presents the implementation of enhanced Hopfield network in hastening the Maximum 3-Satisfiability (MAX-3SAT) logic programming. Four post optimization techniques are investigated, including the Elliot symmetric activation function, Gaussian activation function, Wavelet activation function and Hyperbolic tangent activation function. The performances of these post optimization techniques in accelerating MAX-3SAT logic programming will be discussed in terms of the ratio of maximum satisfied clauses, Hamming distance and the computation time. Dev-C++ was used as the platform for training, testing and validating our proposed techniques. The results depict the Hyperbolic tangent activation function and Elliot symmetric activation function can be...

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

Performance analysis of activation function in higher order logic programming

Higher-order logic programming in Hopfield neural network is a vital paradigm to solve numerous combinatorial optimization problem and pattern recognition. Hence, activation function can be integrated as catalyst or accelerating techniques of doing higher order logic programming in Hopfield network. Obviously, the McCulloch-Pitts learning rule is widely used in higher order logic programming. Hereby, we proposed the Bipolar sigmoid and Hyperbolic activation function trained by Wan Abdullah’s method by integrating energy minimization scheme in order to speed up the training process. Computer simulations are carried out to authenticate the performance of Hyperbolic activation function, Bipolar sigmoid activation function and McCulloch-Pitts function (Logistic Function) in higher order Hopfield network. We used Microsoft Visual C++ 2013 as a platform of simulating, training and testing the network. Therefore, evaluations are made between these activation functions to see which one is superior in the aspects ...