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 statistical natural language processing
Foundations of statistical natural language processing
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
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)
Constant creation in grammatical evolution
International Journal of Innovative Computing and Applications
Analysis of a digit concatenation approach to constant creation
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
An attribute grammar decoder for the 01 multiconstrained knapsack problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
IEEE Transactions on Evolutionary Computation
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Many automatically-synthesized programs have, like their hand-made counterparts, numerical parameters that need to be set properly before they can show an acceptable performance. Hence, any approach to the automatic synthesis of programs needs the ability to tune numerical parameters efficiently. Grammatical Evolution (GE) is a promising grammar-based genetic programming technique that synthesizes numbers by concatenating digits. In this paper, we show that a naive application of this approach can lead to a serious number length bias that in turn affects efficiency. The root of the problem is the way the context-free grammar used by GE is defined. A simple, yet effective, solution to this problem is proposed.