Genetic algortihm to solve pcb component placement modeled as travelling salesman problem

This thesis discuss about Genetic Algorithm to solve PCB component placement modeled as Travelling Salesman Problem (TSP). Genetic algorithms are a class of stochastic search algorithms based on biological evolution. GA represents an iterative process. Each iteration called generation. A typical number of generations for a simple GA can range from 50 to over 500. The entire set of generations is called run. At the end of the run, the result expected is to find one or more highly fit chromosomes. The travelling salesman problem (TSP) is one of the most widely discussed problems in combinatorial optimization. There are cities and distance given between the cities. Travelling salesman has to visit all of them, but he does not to travel very much. Then task is to find a sequence or route of cities to minimize travelling distance and time. The problem statement is to find the most optimum result for TSP problem which means finding the optimum time and distances for the travelling salesman to visit all the cities and return back to his home city. To achieve this result, genetic algorithm technique was used. There are several objectives set for this research which all of them connected to the title itself which is about genetic algorithm as an alternative to solve PCB component modeled as TSP problem. At the end of the project, we will be able to see how genetic algorithm used to get optimize result for TSP