- title = "On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection",
- author = "Gabriel Kronberger and Stephan M. Winkler and Michael Affenzeller and Andreas Beham and Stefan Wagner",
- booktitle = "12th International Conference on Computer Aided Systems Theory, EUROCAST 2009",
- year = "2009",
- volume = "5717",
- series = "Lecture Notes in Computer Science",
- pages = "793--800",
- address = "Las Palmas de Gran Canaria, Spain",
- month = feb # " 15-20",
- publisher = "Springer",
- note = "Revised Selected Papers",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-642-04771-8",
-
DOI = "
doi:10.1007/978-3-642-04772-5_102",
- bibdate = "2009-12-15",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurocast/eurocast2009.html#KronbergerWABW09",
-
abstract = "Genetic programming is a powerful heuristic search
technique that is used for a number of real world
applications to solve amongst others regression,
classification, and time-series forecasting problems. A
lot of progress towards a theoretic description of
genetic programming in form of schema theorems has been
made, but the internal dynamics and success factors of
genetic programming are still not fully understood. In
particular, the effects of different crossover
operators in combination with offspring selection are
largely unknown.
This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.",
- notes = "Acknowledgment G.K. thanks the participants of EuroGP 2009 for the insightful comments and discussions. \cite{Kronberger:2009:eurogp}",
