Todays highly competitive markets tend to favor enterprises, in which business processes are analyzed and optimized regularly, in order to be able to operate in accordance with their business goals. The variety of business process management (BPM) methods applied for this purpose, since the emergence of the concept of business reengineering in the 1990s, ranges from incremental adjustments to radical restructuring. In combination with contemporary workflow automation technology, modern redesign methods are powerful tools for enhancing business performance, enabling companies to maintain a winning margin. Optimization methods that deliver sustainable results using evolutionary approaches, however, are nowadays becoming increasingly popular once again, two decades after continuous improvement paradigms had almost completely been abandoned in favor of revolutionary process redesign. This diploma thesis explores one such evolutionary BPM approach employed in the deep Business Optimization Platform (dBOP), a research prototype, which assists analysts with the selection and application of suitable process improvement techniques. The present work demonstrates an evaluation of dBOP with the help of simulated business scenarios based on real case studies, and documents the types of optimization patterns most readily applied through automated process redesign. For this purpose two business processes, one from a car rental enterprise and one from a health insurance company, are modeled and deployed on a process server, and executed using web services and sample data warehouses based on actual statistics. These processes are then analyzed with dBOP, in order to compare its optimization recommendations with those expected from a human analysts perspective.