Genetic algorithm in hopfield neural network with probabilistic 2 satisfiability

Genetic Algorithm (GA) is to convert the problem-solving process into a process similar to the chromosomal changes in biological evolution using the mathematical method and computer simulation operation. This meta-heuristic algorithm has been successfully applied to system logic and non-system logic programming. In this study, we will explore the role of the Bipolar Genetic Algorithm (GA) in enhancing the learning process of the Hopfield neural network based on the previous study of PRO2SAT, and generate global solutions of the Probabilistic 2 Satisfiability model. The main purpose of the learning phase of the PRO2SAT model is to obtain consistent interpretations and calculate the optimal prominence weights, and the GA algorithm is introduced to improve the ability of PRO2SAT to obtain consistent interpretation using its selection, crossover, and mutation operators, and thus to improve the ability of the logic programming model to get a global solution. In the experimental phase, simulation data are used for result testing, and three performance metrics are used to test the consistency interpretation and global solution acquisition ability of the proposed model, including mean absolute error, logic formula satisfaction ratio, and global minimum ratio. Experimental results show that GA, as a meta-heuristic algorithm, has better searching ability for optimal solution and can effectively assist logic programming.