A memetic framework for cooperative coevolution of recurrent neural networks

Memetic algorithms and cooperative coevolution are emerging fields in evolutionary computation which have shown to be powerful tools for real-world application problems and for training neural networks. Cooperative coevolution decomposes a problem into subcomponents that evolve independently. Memetic algorithms provides further enhancement to evolutionary algorithms with local refinement. The use of crossover-based local refinement has gained attention in memetic computing. This paper employs a cooperative coevolutionary framework that utilises the strength of local refinement via crossover. The framework is evaluated by training recurrent neural networks on grammatical inference problems. The results show that the proposed approach can achieve better performance than the standard cooperative coevolution framework.