The process of learning often consists of Inductive Inference, making generalizations from samples. The problem here is finding generalizations (Grammars) for Formal Languages from finite sets of positive and negative sample sentences. The focus of this paper is on Context-Free Languages (CFL’s) as defined Context-Free Grammars (CFG’s), some of which are accepted by Deterministic Push-Down Automata (D-PDA). This paper describes a meta-language for constructing D-PDA’s. This language is then combined with Genetic Programming to evolve D-PDA’s which accept languages. The technique is illustrated with two favorite CFL’s.