Hybrid Genetic Algorithm Model in Neuro Symbolic Integration
Shehab Abdulhabib Alzaeemi1, Saratha Sathasivam2, Muraly Velavan3

1Shehab Abdulhabib Alzaeemi1*, School of Mathematical Science, Universiti Sains Malaysia,  USM, Penang Malaysia.
2Saratha Sathasivam, School of Mathematical Science, Universiti Sains Malaysia, USM, Penang Malaysia.
3Muraly Velavan, School of General and Foundation Studies, AIMST University, Semeling, Kedah.

Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 2144-2149 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8761049420/2020©BEIESP | DOI: 10.35940/ijeat.D8761.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The development of artificial neural network and logic programming plays an important part in neural network studies. Genetic Algorithm (GA) is one of the escorted randomly searching technicality that uses evolutional concepts of the natural election as a stimulus to solve the computational problems. The essential purposes behind the studies of the evolutional system is for developing adaptive search techniques which are robust. In this paper, GA is merged with agent based modeling (ABM) by using specified proceedings to optimise the states of neurons and energy function in the Hopfield neural network (HNN). Hence, it is observed that the GA provides the best solutions in affirming optimal states of neurons and thus, enhancing the performance of Horn Satisfiability logical program (HornSAT) in Hopfield neural network. This is due to the fact that the GA lesser susceptive to be restricted in the local optimal or in any suboptimal solutions. NETLOGO version 6.0 will be used as a dynamic platform to test our proposed model. Hence, the computer simulations will be carried out to substantiate and authenticate the efficiency of the proposed model. The results are then tabulated by evaluating the global minimum ratio, computational time and hamming distance.
Keywords: Logic Program, Genetic Algorithm, Hopfield Neural Network, Horn Satisfiability, NETLOGO, Agent Based Modelling.