Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Finding Needles in Haystacks Is Not Hard with Neutrality
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
Meta-grammar constant creation with grammatical evolution by grammatical evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Grammatical Swarm: The generation of programs by social programming
Natural Computing: an international journal
On the locality of grammatical evolution
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
mGGA: the meta-grammar genetic algorithm
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Constituent grammatical evolution
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Analysing the effects of diverse operators in a genetic programming system
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Combining technical analysis and grammatical evolution in a trading system
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. Two variants of genotype representation are found in the literature, namely, binary and integer forms. For the first time we analyse and compare these two representations to determine if one has a performance advantage over the other. As such this study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation operators, improve GE's efficiency and effectiveness. Four mutation operators using two different representations, binary and gray code representation, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The results provide support for the use of an integer-based genotypic representation as the alternative representations do not exhibit better performance, and the integer representation provides a statistically significant advantage on one of the three benchmarks. In addition, a novel wrapping operator for the binary and gray code representations is examined, and it is found that across the three problems examined there is no general trend to recommend the adoption of an alternative wrapping operator. The results also back up earlier findings which support the adoption of wrapping.