ABSTRACT:Genetic algorithms (GAs) are one of the guided random search techniques that use evolutionary ideas of natural selection as an inspiration for solving computational problems. The basic idea behind the study of evolutionary systems is to develop a robust and adaptive search technique. GAs can be used as an optimization tool for engineering problems and other real world complex problems. In this paper, we carried out computer simulations using a developed agent based modelling (ABM) from NETLOGO as the platform to demonstrate and verify the ability of doing logic programming in Hopfield network. GAs was also incorporated into the developed agent based modelling (ABM) using specific procedures to optimize neuron states and energy in the Hopfield network. We then analyzed the GAs performance by comparing the results of global minima ratio, computational time and hamming distance of the GAs with the previous method proposed by Wan Abdullah. We assumed that this is due to the fact that GAs is less susceptible to been trapped in local optima or in any sub-optimal solutions. Hence, it is observed that GAs provide better solutions in finding optimal neuron states and thus, enhance the performance of doing logic programming in Hopfield network.
KeyWords: Hopfield network, Genetic Algorithms, Agent Based Modelling, Logic Program.
How to cite: Shehab Abdulhabib Saeed Alzaeemi et al. Use of Genetic Algorithm for Hopfield Neural Network to do Logic Programming. Bio-Genetics J. 5(1) 2017: 101-113