%0 Conference Paper %1 luke:1997:ccmGP %A Luke, Sean %A Spector, Lee %B Genetic Programming 1997: Proceedings of the Second Annual Conference %C Stanford University, CA, USA %D 1997 %E Koza, John R. %E Deb, Kalyanmoy %E Dorigo, Marco %E Fogel, David B. %E Garzon, Max %E Iba, Hitoshi %E Riolo, Rick L. %I Morgan Kaufmann %K algorithms, genetic programming %P 240--248 %T A Comparison of Crossover and Mutation in Genetic Programming %U http://www.cs.gmu.edu/~sean/papers/comparison/comparison.ps.gz %X This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of "building blocks" in GP.

@inproceedings{luke:1997:ccmGP, abstract = {This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of {"}building blocks{"} in GP.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Stanford University, CA, USA}, author = {Luke, Sean and Spector, Lee}, biburl = {https://www.bibsonomy.org/bibtex/2ce579d7373273310d89ecc78a0dd6b2f/brazovayeye}, booktitle = {Genetic Programming 1997: Proceedings of the Second Annual Conference}, editor = {Koza, John R. and Deb, Kalyanmoy and Dorigo, Marco and Fogel, David B. and Garzon, Max and Iba, Hitoshi and Riolo, Rick L.}, figures = {http://www.cs.gmu.edu/~sean/papers/comparison/figures1-2.ps.gz}, interhash = {d1a0df22d7d69cc22dbc739b30dc851e}, intrahash = {ce579d7373273310d89ecc78a0dd6b2f}, keywords = {algorithms, genetic programming}, month = {13-16 July}, notes = {GP-97. 6-mux, lawn mower, symbolic regression, Santa Fe trail artificial ant. See alse \cite{luke:1998:rcxmGP}. The Gzipped PostScript version (.ps.gz) does not come with figures; to get the figures for the PostScript version, use the figures URLs below}, pages = {240--248}, publisher = {Morgan Kaufmann}, publisher_address = {San Francisco, CA, USA}, timestamp = {2008-06-19T17:45:56.000+0200}, title = {A Comparison of Crossover and Mutation in Genetic Programming}, url = {http://www.cs.gmu.edu/~sean/papers/comparison/comparison.ps.gz}, year = 1997 }