Evolving Complex Structures via Cooperative Coevolution
Kenneth Alan De Jong, Mitchell A. Potter · The MIT Press eBooks · 1995
A cooperative coevolutionary approach to learning complex structures is presented which, although preliminary in nature, appears to have a number of advantages over non-coevolutionary approaches. The cooperative coevolutionary approach encourages the parallel evolution of substructures which interact in useful ways to form more complex higher level structures. The architecture is designed to be general enough to permit the inclusion, if appropriate, of a priori knowledge in the form of initial biases towards particular kinds of decompositions. A brief summary of initial results obtained from testing this architecture in several problem domains is presented which shows a significant speedup over more traditional non-coevolutionary approaches. 1 INTRODUCTION For both natural and artificial systems the ability to evolve complex structures is desirable, but difficult to achieve. Our conventional evolutionary algorithms typically provide performance-oriented feedback and as a consequence ...