Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.

Wireless Internet technologies have been developing and users are now able to access more information anywhere through small screen mobile devices. However, due to the limits of cost, bandwidth and screen size in a wireless environment, it is important to minimize interactions between a mobile user and his handheld device, as well as the amount of data transmitted. In this paper we present an interactive evolutionary approach for user-oriented Web search by using mobile devices. To verify this approach, a series of experiments has been conducted. The results show that our approach can allocate the information a user needs within only a few user-system interactions. It substantially reduces the number of retrieved pages a user has to visit. This is especially an important benefit to mobile users.