@incollection{Branke2005, abstract = {Many real-world optimization problems involve multiple, typically conflicting objectives. Often, it is very difficult to weigh the different criteria exactly before alternatives are known. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions. However, often the user has at least a vague idea about what kind of solutions might be preferred. In this chapter, we argue that such knowledge should be used to focus the search on the most interesting (from a user's perspective) areas of the Paretooptimal front. To this end, we present and compare two methods which allow to integrate vague user preferences into evolutionary multi-objective algorithms. As we show, such methods may speed up the search and yield a more fine-grained selection of alternatives in the most relevant parts of the Pareto-optimal front.}, author = {Branke, J{\"{u}}rgen and Deb, Kalyanmoy}, doi = {10.1007/978-3-540-44511-1_21}, mendeley-groups = {COIN{\_}website/2004}, pages = {461--477}, publisher = {Springer, Berlin, Heidelberg}, title = {{Integrating User Preferences into Evolutionary Multi-Objective Optimization}}, url = {https://link.springer.com/chapter/10.1007/978-3-540-44511-1{\_}21}, year = {2005} }