- author = "Maarten Keijzer and Conor Ryan and Michael O'Neill and Mike Cattolico and Vladan Babovic",
- title = "Ripple Crossover in Genetic Programming",
- booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
- year = "2001",
- editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon",
- volume = "2038",
- series = "LNCS",
- pages = "74--86",
- address = "Lake Como, Italy",
- publisher_address = "Berlin",
- month = "18-20 " # apr,
- organisation = "EvoNET",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, Context Free Grammars, Crossover, Intrinsic Polymorphism",
- ISBN = "3-540-41899-7",
-
DOI = "
doi:10.1007/3-540-45355-5_7",
- size = "13 pages",
-
abstract = "This paper isolates and identifies the effects of the
crossover operator used in Grammatical Evolution. This
crossover operator has already been shown to be adept
at combining useful building blocks and to outperform
engineered crossover operators such as Homologous
Crossover. This crossover operator, Ripple Crossover is
described in terms of Genetic Programming and applied
to two benchmark problems.
Its performance is compared with that of traditional sub-tree crossover on populations employing the standard functions and terminal set, but also against populations of individuals that encode Context Free Grammars. Ripple crossover is more effective in exploring the search space of possible programs than sub-tree crossover. This is shown by examining the rate of premature convergence during the run. Ripple crossover produces populations whose fitness increases gradually over time, slower than, but to an eventual higher level than that of sub-tree crossover.",
- notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
