Evolving context-free language predictors

Recurrent neural networks can represent and process simple context-free languages. However, the diiculty of nding with gradient-based learning appropriate weights for context-free language prediction motivates an investigation on the applicability of evolutionary algorithms. By empirical studies , an evolutionary algorithm proves to be more reliable in nding prediction solutions to a simple CFL. Moreover, the evolutionary algorithm demonstrates greater diversity by making use of a larger repertoire of dynami-cal behaviors for solving the problem.