Evolutionary Algorithms for

Many real-world problems involve two types of problem difficulty: i) multiple, conflicting objectives and ii) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Paretooptimal solutions concurrently in a single simulation run. However, in spite of this variety, there is a lack of extensive comparative studies in the literature. Therefore, it has remained open up to now

Die Thalschaft St. Antönien im Prättigau in ihren wirtschaftlichen und pflanzengeographischen Verhältnissen

von C. Schröte

Aufnahmen der Bauschule am Genfersee und in St.Gallen

unter Leitung von Karl MoserAm Kopf des Titelblattes: Eidgen. Techn. Hochschul

Tessinkorrektion : Gegenwärtiger Stand der künstlichen und natürlichen Bewaldung des ehemaligen Flussbettes

Reproduktion nach dem Original des Geobot. Institutes, Bl. VK 1