Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Tree adjoining grammars, language bias, and genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Christiansen Grammar Evolution: Grammatical Evolution With Semantics
IEEE Transactions on Evolutionary Computation
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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.