Conor Ryan
2001
EuroGP 2001, pages 337-347, 2001
We present an investigation into crossover in Grammatical Evolution that begins by examining a biologically-inspired homologous crossover operator that is compared to standard one and two-point operators. Results demonstrate that this homologous operator ...
EvoWorkshops 2001, pages 343-352, 2001
This study examines the potential of an evolutionary automatic programming methodology to uncover a series of useful technical trading rules for the UK FTSE 100 stock index. Index values for the period 26/4/1984 to 4/12/1997 are used to train and test the model. The ...
2000
EuroGP 2000, pages 149-162, 2000
Abstract. Grammatical Evolution is an evolutionary algorithm which can produce code in any language, requiring as inputs a BNF grammar definition describing the output language, and the fitness function. The usefulness of crossover in GP systems has been hotly debated for ...
1999
Genetic Code Degeneracy: Implications for Grammatical Evolution and BeyondPDF
ECAL99, pages 149-153, 1999
Grammatical Evolution (GE) is a grammar-based GA which generates computer programs. GE has the distinction that its input is a BNF, which permits it to generate programs in any language, of arbitrary complexity. Part of the power of GE is that it is closer to natural DNA ...
1998
EuroGP 1998, pages 83-96, 1998
We describe a Genetic Algorithm that can evolve complete programs. Using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved. Other automatic programming ...MORE ⇓
We describe a Genetic Algorithm that can evolve complete programs. Using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved. Other automatic programming methods are described, before our system, Grammatical Evolution, is applied to a symbolic regression problem.