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The shortest/optimal path planning in a static environment is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. In this paper, the problem of finding the optimal collision free path in complex environments for a mobile robot is solved using a hybrid neural network, Genetic Algorithm and local Search method. We constructed the neural network model of environmental and used this model to establish the relationship between a collision avoidance path and the output of the model. What is new in this work is a novel representation of solutions for evolutionary algorithms that is efficient, simple and also compatible with Hybrid algorithm. The new representation makes it possible to solve the problem with a small population and in a few generations. It also makes the genetic operator simple and allows using an efficient local search operator within the evolutionary algorithm. The performance of the proposed GA approach is tested on eight different environments consisting of polygonal obstacles with increasing complexity.