Evolutionary algorithms, including genetic algorithms, evolutionary programming, and evolutionary strategies, are search and learning algorithms that were first developed in the 1960's and 1970's. Based on principles from genetics and evolutionary biology, these algorithms have been successfully applied to a wide range of problems. Our research includes both theoretical studies on how these algorithms work and real world applications.

Complex adaptive systems are large-scale decentralized systems consisting of many independent agents at the micro-level that together form a coherent whole at the macro-level. Of particular interest is the fact that the macro-level may have emergent properties; that is, the macro-level system can exhibit characteristics and abilities that are not present or predicted from micro-level behavior. Our research explores topics in swarm intelligence, decentralized coordination and task allocation, collective control, and cellular automata.