Archivers for Multi-objective Optimization

Multi-objective optimization problems have a set of optimal solutions. An archiver is the component of an evolutionary algorithm that decides which solutions to keep during the search. This project studies the theoretical properties of archivers (convergence, approximation quality, and limit behavior) and develops new archiving strategies with provable guarantees. A Springer monograph summarizes the work, and several publications in IEEE Transactions on Evolutionary Computation report its results. The project is a collaboration with Dr. Oliver Schütze, CINVESTAV-IPN.