Our research involves complex adaptive systems and evolutionary computation. For a computational system to be adaptive, it must respond to pressures from the environment on short and long time scales, while still maintaining an appropriate sense of its (possibly changing) function in that environment. If myriad microbehaviors give rise to useful emergent behavior, then short-term adaptation can be made possible through the regulation of the available microbehaviors. This is, for example, what happens with the credit assignment in classifier systems. Long-term adaptation can be manifest using an evolutionary procedure for varying the set of possible microbehaviors. We are working to combine these two in an emergent architecture called Starcat, which derives some of its design from the Copycat program of Mitchell and Hofstadter. That architecture uses dynamically reconfiguring network of domain concepts to shape the emergent behavior of the system. One application of this architecture supported exploration of ant-colony algorithm behavior. Underway are several other applications, including an A-life simulation, pattern perception in music, robot navigation and mapping, and the original Copycat. We have paid special attention in the design to asynchronous behavior of the components and generality of domain.