Duplication of modules facilitates the evolution of functional specialization
The evolution of simulated robots with three different architectures is studied in this article. We compare a nonmodular feed-forward network, a hardwired modular, and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high-level behavioral Functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of Functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state.
modularity genetic duplication neural networks genetic algorithms adaptive behavior