Highly complex and interconnected systems may suffer from intermittent or transient failures that are particularly difficult to diagnose. This research focuses on the use of genetic algorithms for automatically generating large volumes of software test cases. In particular, the paper explores two fundamental strategies for improving the performance of genetic algorithm test case breeding for high volume testing. The first strategy seeks to avoid evaluating test cases against the real target system by using oracles or models. The second strategy involves improving the more costly components of genetic algorithms, such as fitness function calculations. Together, the various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.