In this study, a hybrid approach that employs Hopfield neural network and a genetic algorithm in
doing k-SAT problems was proposed. The Hopfield neural network was used to minimise logical
inconsistency in interpreting logic clauses or programme. Hybrid optimisation made use of the global
convergence advantage of the genetic algorithm to deal with learning complexity in the Hopfield
network. The simulation incorporated with genetic algorithm and exhaustive search method with different
k-Satisfiability (k-SAT) problems, namely, the Horn-Satisfiability (HORN-SAT), 2-Satisfiability (2-SAT)
and 3-Satisfiability (3-SAT) will be developed by using Microsoft Visual C++ 2010 Express Software.
The performance of both searching techniques was evaluated based on global minima ratio, hamming
distance and computation time. Simulated results suggested that the genetic algorithm outperformed
exhaustive search in doing k-SAT logic programming in the Hopfield network.