Abstract : This project contributed new principles for the development of intelligent, mobile robots performing complex tasks in unpredictable environments. In the behavior-based approach to robot design, the overall performance of the robot arises through the interaction of multiple, relatively simple, behaviors. The manual design of multiple interacting behaviors is difficult, labor-intensive and error-prone. One way to reduce the effort in the design of behavior-based robots is to develop an evolutionary approach in which the various behaviors, as well as their modes of interaction, evolve over time. Evolution may also provide a basis for the development of strategies for multiple-robot environments, for example, environments in which a robot is expected to adapt its behavior based on the current behavior of other agents or environmental conditions which themselves are changing over time. This project addressed in four complementary areas concerning the effectiveness of evolutionary algorithms for the design of autonomous robots: (1) learning multiple behaviors by asynchronous co-evolution; (2) continuous and embedded learning; (3) comparison with other reinforcement learning methods, and (4) the ability to evolve responses to changing environments. Results in each of these tasks are reported.