Christian Johansson; Gustav Evertsson MSE-2003-09, pp. 42. Inst. för programvaruteknik och datavetenskap/Dept. of Software Engineering and Computer Science, 2003.
Genetic algorithms have a lot of properties that makes it a good choice when one needs to solve very complicated problems. The performance of genetic algorithms is affected by the parameters that are used. Optimization of the parameters for the genetic algorithm is one of the most popular research fields of genetic algorithms. One of the reasons for this is because of the complicated relation between the parameters and factors such as the complexity of the problem. This thesis describes what happens when time constraints are added to this problem. One of the most important parameters is population size and we have found by testing a well known set of optimization benchmark problems that the optimal population size is not the same when time constraints were involved.