Betreuer: Pradyumn Kumar Shukla, Hartmut Schmeck
Forschungsgruppe: Effiziente Algorithmen
Abgabe: 11. Juli 2011
For many years evolutionary algorithms have shown to be capable of solving multi-objective optimization problems. Nearly all of these algorithms aim at finding an approximation of the Pareto optimal front, the set of solutions that are not dominated by any other selection of decision variables. Considering user preferences, however, not all Pareto optimal solu- tions necessarily present an interesting choice. The concept of proper Pareto optimality tries to bound the trade-off between objective values. The notion has been successfully im- plemented in deterministic search methods, but has so far only received little attention in evolutionary optimization. This thesis proposes modifications of existing state-of-the-art algorithms that guide the search towards proper Pareto optimal solutions. An extensive set of different benchmark problems is used to assess the performance of these algorithms. Computational results indicate that the modified algorithms are able to find the complete solution set of preferred regions.