Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a rigorous experimental design. We use one of the largest publicly accessible music listening histories dataset to analyse if the methodology works well for large real-world tasks. The dataset has been used to simulate various real-world scenarios for the experimental design. The methodology fuses the recommendations generated by an unspecified number of recommenders. In this study, we have used three recommenders, which have specialised goals in terms of user-centric metrics. We use three different Evolutionary Algorithms to analyse the capability of different EA strategies for generating a near-optimal set of trade-offs on user-centric metrics. We have performed elaborate qualitative and quantitative analyses of the system to understand how various aspects of the system affect the final set of solutions.

Software testing is an important, well-researched field. With the majority of modern-day applications using relational databases to manipulate their data, it is crucial that database interactions are tested as well. This is a complex task to perform manually, and thus researchers have been attempting to tackle this problem by means of automated test data generation. In their studies, they apply constraint-based techniques using SAT solvers to generate the test data. However, these techniques have known limitations such as solving subqueries. In this thesis, we present a novel search-based approach that uses a Genetic Algorithm to generate test data for SQL queries, which overcomes the limitations of previous research. We provide an implementation of our approach, EvoSQL. In our implementation, we instrument a real database to extract all the information necessary for the fitness function. By doing so, we support all queries using standard SQL syntax. We evaluate our approach on 2,135 queries from 4 real-world systems, of which EvoSQL is able to cover over 96% fully.