Published Paper Details:

Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. Researchers have been trying to understand the mechanics of GA parameter interactions by using various techniques. It still remains an open question for practitioners as to what values of GA parameters (such as population size, choice of GA operators, operator probabilities, and others) to use in an arbitrary problem. Genetic algorithm (GA) parameters are explored to minimize the time needed to find a solution. The basic GA code stays the same throughout the entire system. Variable parameters include mutation rate, crossover rate, crossover operator, number of generations, population size, etc. When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria, selection operator and pressure, and fitness function to be used in order to solve the problem. This paper tests a relationship between (1) size of the initial population, (2) mutation probability, and (3) number of generations in runs of Genetic Algorithm for solving the TSP.TSP is an NP-hard problem, so using the Genetic Algorithm we can find a solution in a reasonable amount of time. In this thesis we describe the results of the solution for the Traveling salesman problem (TSP) for Kosovo municipalities, using the genetic algorithm, with different settings for the parameters of the Genetic Algorithm.