Genetic algorithms and neural networks are two tools in a growing arsenal of computational methods being applied to fundamental issues of theoretical biology. Much of this work is now being called ``artificial life'' (ALife). I was asked by the editor of IEEE Expert for a survey article that motivates this area for its pragmatic audience of AI expert system engineers . I argue here that there are striking parallels between AI's investigation of intelligence and ALife's investigation of life. Further, ALife represents an important new ``grounding'' of our notions of intelligence in fundamental abilities that arise directly from what it means to be alive. I have also focused on the special role computer science has to a science of ALife, above and beyond simply toolsmithing for biologists .
A recurring theme in much ALife work is the ``evolution of complexity,'' i.e., the development of progressively more elaborate adaptive responses to increasingly difficult environments. A central research issue, therefore, is a careful characterization of what it means to be a more complex environment. Towards this purpuse, we have developed a simulation environment we call Latent Energy Environments (LEE)  and used it to investigate to important interactions between the cognition of individuals and the evolution of populations: evolution of the sensors by which individuals perceive their world  and evolution of ``life history'' characteristics such as when an organism is most susceptible to imitative learning and when it becomes reproductively mature .