Performance of simple genetic algorithm on randomly generated problems

This thesis presents a way to measure performance of simple genetic algorithm (SGA) and investigates the distribution of hard and easy problems for the SGA. We define a function that measures performance level of SGA by comparing best solution after each generation to the optimal solution. We test performance of SGA on random problems and investigate the relationship of the distribution of hard and easy problems to time, space, and performance.