Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An Indirect Block-Oriented Representation for Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Crossover in Grammatical Evolution: The Search Continues
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Grammatical evolution of a robot controller
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Object-level recombination of commodity applications
Proceedings of the 12th annual conference on Genetic and evolutionary computation
jGE: a Java implementation of grammatical evolution
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Evolving high-level imperative program trees with strongly formed genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Vector-valued function estimation by grammatical evolution for autonomous robot control
Information Sciences: an International Journal
| Hi-index | 0.00 |
We describe a Genetic Algorithm called Grammatical Evolution (GE) that can evolve complete programs in an arbitrary language using a variable length linear genome. The binary genome determines which production rules in a Backus Naur Form grammar definition are used in a genotype to phenotype mapping process to a program. Expressions and programs of arbitrary complexity may be evolved using this system. Since first describing this system, GE has been applied to other problem domains, and during this time GE has undergone some evolution. This paper serves to report these changes, and also describes how we evolved multi-line C-code to solve a version of the Santa Fe Ant Trail. The results obtained are then compared to results produced by Genetic Programming, and it is found that GE outperforms GP on this problem.