A Survey and Comparison of Tree Generation Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Luke1:2001:GECCO,

• title = "A Survey and Comparison of Tree Generation Algorithms",
• author = "Sean Luke and Liviu Panait",
• pages = "81--88",
• year = "2001",
• publisher = "Morgan Kaufmann",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)",
• editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke",
• address = "San Francisco, California, USA",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "7-11 " # jul,
• keywords = "genetic algorithms, genetic programming, tree generation algorithms, initalization",
• ISBN = "1-55860-774-9",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf",
• URL = "https://dl.acm.org/doi/10.5555/2955239.2955250",
• size = "8 pages",
• abstract = "This paper discusses and compares five major tree-generation algorithms for genetic programming, and their effects on fitness: RAMPED HALF-AND-HALF, PTC1, PTC2, RANDOM-BRANCH, and UNIFORM. The paper compares the performance of these algorithms on three genetic programming problems (11-Boolean Multiplexer, Artificial Ant, and Symbolic Regression), and discovers that the algorithms do not have a significant impact on fitness. Additional experimentation shows that tree size does have an important impact on fitness, and further that the ideal initial tree size is very different from that used in traditional GP.",
• notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}",

}

Genetic Programming entries for Sean Luke Liviu Panait

Citations