Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
Is The Perfect The Enemy Of The Good?
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolving Multi-line Compilable C Programs
Proceedings of the Second European Workshop on Genetic Programming
Grammatical Evolution Rules: The Mod and the Bucket Rule
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Genetic Algorithms Using Grammatical Evolution
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Probabilistic incremental program evolution
Evolutionary Computation
Bayesian automatic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
IEEE Transactions on Evolutionary Computation
Constituent grammatical evolution
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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This paper focuses on methodological problems associated to the famous Santa Fe Trail (SFT) problem, a very common benchmark for evaluating Genetic Programming (GP) algorithms, introduced by Koza in its first book on GP. We put in evidence the difficulty to ensure fair comparisons especially with new genotype representations as found in works on grammar-based automatic programming, such as Grammatical Evolution, and Bayesian Automatic Programming. We extend a work by Langdon et al. by measuring the effort to solve SFT by random search with different time steps limits and a reduced but semantically equivalent function set.