Evolving adaptive neural networks with and without adaptive synapses

A potentially powerful application of evolutionary computation (EC) is to evolve neural networks for automated control tasks. However, in such tasks environments can be unpredictable and fixed control policies may fail when conditions suddenly change. Thus, there is a need to evolve neural networks that can adapt, i.e. change their control policy dynamically as conditions change. In this paper, we examine two methods for evolving neural networks with dynamic policies. The first method evolves recurrent neural networks with fixed connection weights, relying on internal state changes to lead to changes in behavior. The second method evolves local rules that govern connection weight changes. The surprising experimental result is that the former method can be more effective than evolving networks with dynamic weights, calling into question the intuitive notion that networks with dynamic synapses are necessary for evolving solutions to adaptive tasks.