This paper explores the use of a real-valued modular genetic algorithm to evolve continuous-time recurrent neural networks capable of sequential behavior and learning. We evolve networks that can generate a fixed sequence of outputs in response to an external trigger occurring at varying intervals of time. We also evolve networks that can learn to generate one of a set of possible sequences based upon reinforcement from the environment. Finally, we utilize concepts from dynamical systems theory to understand the operation of some of these evolved networks. A novel feature of our approach is that we assume neither an a priori discretization of states or time nor an a priori learning algorithm that explicitly modifies network parameters during learning. Rather, we merely expose dynamical neural networks to tasks that require sequential behavior
and learning and allow the genetic algorithm to evolve network dynamics capable of accomplishing these tasks.
Yamauchi, B. and Beer, R.D. (1994). Sequential behavior and learning in evolved dynamical neural networks. Adaptive Behavior 2(3):219-246.