Coevolutionary Principles

Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by optimization or evolutionary algorithms. The purpose of this chapter is to provide a foundational understanding of coevolutionary algorithms and to highlight critical theoretical and empirical work done over the last two decades. The chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it. Elena Popovici Icosystem Corporation, 10 Fawcett St., Cambridge, MA USA e-mail: [email protected] Anthony Bucci Icosystem Corporation, 10 Fawcett St., Cambridge, MA USA e-mail: [email protected] R. Paul Wiegand Institute for Simulation & Training, University of Central Florida, Orlando, FL USA e-mail: [email protected] Edwin D. de Jong Institute of Information and Computing Sciences, Utrecht University, The Netherlands e-mail: [email protected]