ROURKELA is an authentic work carried out by them under my supervision and guidance. To the best of my knowledge, the matter embodied in the thesis has not been submitted to any other university/institute for the award of any degree or diploma. Acknowledgement We would like to express our deepest of gratitude to our project guide, Prof. Rourkela for giving us this wonderful opportunity to work under him and to provide us with valuable guidance and the necessary support throughout the course of this project. We would also like to express our sincere thanks to Mr. Prakash Kumar Rout (VLSI Lab, NIT Rourkela) for helping us throughout the development process of this project. project evaluation panel for their constructive criticism, advices to change the way of approaching the problem where ever required and constant inspiration. We would like to express our gratitude towards our faculty members for providing a solid background for our studies and research thereafter which helped us a lot to properly shape the problem and provided insights towards the solution. They have been great sources of inspiration to us and we thank them from the bottom of our heart. Also, we are extremely thankful to our family for the enormous support and encouragement they have always provided us with, which has been our source of strength throughout the life. Last but not least, we would like to thank our friends who supported us and everyone else who directly or indirectly helped us in our project. Abstract A modified approach for the application of Genetic Algorithm (GA) to the Channel Routing Problem has been proposed. The code based on the algorithm proposed in  has been implemented for the GA procedures of Initial Population Generation, Crossover, Mutation and Selection. A few improvements over the existing work have been made and the results so far obtained have been encouraging. Further experimentation is being done on the algorithm and other ideas generated during the development of the code are being implemented for faster convergence of the algorithm and for generation of more efficient results. Also application of variations of the GA technique like Vector GA and even other computationally intelligent techniques like Particle Swarm Optimization to the channel routing problem is being thought of.