This paper introduces an approach to evolving computer programs using an Attribute Grammar (AG) extension of Grammatical Evolution (GE) to eliminate ineffective pieces of code with the help of context-sensitive information.The standard Context-Free Grammars (CFGs) used in GE, Genetic Programming (GP) (which uses a special type of CFG with just a single non-terminal) and most other grammar-based system are not well-suited for codifying information about context. AGs, on the other hand, are grammars that contain functional units that can help determine context which, as this paper demonstrates, is key to removing ineffective code.The results presented in this paper indicate that, on a selection of grammars, the prevention of the appearance of ineffective code through the use of context analysis significantly improves the performance of and resistance to code bloat over both standard GE and GP for both Santa Fe Trail (SFT) and Los Altos Hills (LAH) trail version of the ant problem with same amount of energy used.