Abstract- This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a classic control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem "cart-pole”. In this paper we have tried to train the Artificial Neural Network (ANN) with using Genetic Algorithms (program is written in MATLAB) which is compared with the output obtained using the Artificial Neural Network Toolbox provided in MATLAB. In proposed approach we have used both ANNs and Genetic Algorithm to get more optimal solution. Here we applied the approach for the Fuzzy logic technique to design a Fuzzy Logic Controllers (FLC) using ANNs and Genetic Algorithm (GA). The Fuzzy rules which are needed to control the problem will be framed with the combination of Artificial Neural Networks and Genetic Algorithm. It has been found that such a searching technique converges intelligently and much faster than conventional learning means. Performance of the presented neural network training using the genetic algorihtms is much better and providing more accurate results. Keywords: Artificial Neural Network, Genetic Algorithm, Fuzzy Logic Controllers. I. INTRODUCTION The control of a cart-pole system is widely used as a benchmark problem for testing the efficiency of reinforcement learning algorithms [8]. Artificial Neural Networks can be trained to simulate the execution of the rule base of the Fuzzy Logic Controllers (FLC) using Genetic Algorithms(GA) for determining the solution of the cart-pole problem (program is written in MATLAB).The genetic design approach discussed in this paper offers a convenient and complete way to design a fuzzy controller in the shortest time. We are expecting better solution in terms of number of iterations, performance of the presented neural network training using the genetic algorihtms must be much better with more accurate results. Again in terms of requirement of fewer epochs for training the ANN using GA comparing to ANN toolbox provided in MATLAB. We will describe two earlier applications of genetic algorithms to the automatic generation of a fuzzy rule base, and compare these with the ANN toolbox output to our approach. One of the earliest applications of genetic algorithms to the design of fuzzy systems was developed by Karr (1991), again to solve the cart-pole problem [2]. The production of the fuzzy controller begins with the definition of the fuzzy sets used to describe each input variable. Each of the four input variables are characterized by three fuzzy sets—NEGATIVE, ZERO, and POSITIVE—yielding 81 possible combinations. The fuzzy system designer then assigns one of seven choices for the output to each input combination. The resulting fuzzy system represents the expert's "best guess". The membership function extrema are then encoded into a bit string, and a genetic algorithm is applied to shift the membership functions so as to find locations which improve performance. The evolved system consistently outperforms the original, being capable of recovering from initial positions that fail under the original rule base. In second application, Genetic algorithms for automatic design of fuzzy logic controllers have been developed [9], using sophisticated membership functions that intrinsically reflect the nonlinearity encountered in many engineering applications. But the sophistication obtained by the machine based automatic design could not be reached by manual design which is exclusively based on a painstaking trial-and-error process. As the number of variables increases the length of the string encoding the system size increases exponentially, with a corresponding exponential increase in the complexity of the search space. Such a system is unlikely to scale well for more complex problems. We are using ANNs and Genetic Algorithm both to get more optimal solution. Here we applied the approach for the fuzzy logic technique to design a Fuzzy Logic Controller (FLC) using ANNs and Genetic Algorithm (GA). The resultant optimal fuzzy logic controller is used in centering a cart. Artificial Neural Networks can be trained to simulate the execution of the rule base of the Fuzzy Logic Controllers (FLC) using Genetic Algorithms. Also the training of artificial neural networks using genetic algorithms is extended to include a priori control knowledge of human operators in the form of rule base table. It is shown that the system can solve more concretely a fairly difficult control learning problem. It also demonstrated the feasibility of the method when applied to a cart-pole balancing problem. The performance of the GA and ANN Optimized Fuzzy Logic controller is compared with that of the conventional ANN EVOLUTIONARY DESIGN OF FUZZY LOGIC CONTROLLERS WITH THE TECHNIQUES ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM FOR CART-POLE PROBLEM Reena Thakur 1 , Vinay Kr Singh 2 , Manu Pratap Singh 3 1 Department of Information Technology, AEC, Agra, India 2 Department of Information Technology, AEC, Agra, India 3 Department of Computer Science, ICIS, Dr. B.R. Ambedkar University, Khnadari Campus, Agra, India ( 1 rina151174@rediffmail.com, 2 vksingh100@rediffmail.com, 3 manu_p_singh@hotmail.com) 978-1-4244-5967-4/10/$26.00 ©2010 IEEE