Theses and Dissertations Collection

This dissertation is about understanding the requirements for successfully implementingTransfer Learning (TL) in the Genetic Algorithms (GA). TL is the procedure of transferring previous knowledge from an old problem, called the source problem (S) to another problem called the target problem (T). We have performed this study by implementing the process of the TL by employing the Genetic Algorithm (GA) as the model solver. GA is a type of Evolutionary Computation (EC) inspired by biological evolution theory that using biological evolution strategies by mimicking inheriting characteristics over many generations. TL has some limitations, for example, negative transfer. This situation halts the performance of solving the target problem. Also, during our study, we found out transferring the whole final source population to the target problem is not always a beneficial strategy for solving hard or non-related problems. Our study focuses on understanding the behavior of the transferred population and how to make them more beneficial to the target solver and the GA. In this dissertation, we experimented with and evaluated several strategies for transferring knowledge including the Estimation of Distribution Algorithm (ED). We proposed an algorithm that samples the transferred population, and we evaluated our algorithm against other strategies of TL. We experimented and analyzed the effect of the content of the transferred population on the performance of the target solver. We experimented with transferring partial knowledge from the source problem to the target problem. We also experimented with sampling and transferring knowledge from multiple source problems to the target problem. The results of our studies show how TL can improve the performance of the GA in terms of the number of generations, time, effort the GA solver took to find the optimal solution. Also, analyzed factors that affect the GA performance and how to sample transferred population in terms of providing the GA with needed knowledge from the previous problem.