Quadratic Bloat in Genetic Programming

author = "W. B. Langdon",

title = "Quadratic Bloat in Genetic Programming",

pages = "451--458",

year = "2000",

publisher = "Morgan Kaufmann",

booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)",

editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",

address = "Las Vegas, Nevada, USA",

publisher_address = "San Francisco, CA 94104, USA",

month = "10-12 " # jul,

keywords = "genetic algorithms, genetic programming, bloat, introns, ineffective code, evolution of shape, subquadratic length growth, linear depth growth, binary tree search spaces",

ISBN = "1-55860-708-0",

URL = "

http://gpbib.cs.ucl.ac.uk/gecco2000/GA069.pdf",

URL = "

http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gecco2000.quad.ps.gz",

URL = "

http://gpbib.cs.ucl.ac.uk/gecco2000/GA069.ps",

URL = "

http://citeseer.ist.psu.edu/316810.html",

URL = "

http://dl.acm.org/citation.cfm?id=2933718.2933802",

acmid = "2933802",

size = "8 pages",

abstract = "In earlier work we predicted program size would grow in the limit at a quadratic rate and up to fifty generations we measured bloat O(generations**(1.2-1.5)). On two simple benchmarks we test the prediction of bloat O(generations**2.0) up to generation 600. In continuous problems the limit of quadratic growth is reached but convergence in the discrete case limits growth in size. Measurements indicate subtree crossover ceases to be disruptive with large programs (1,000,000) and the population effectively converges (even though variety is near unity). Depending upon implementation, we predict run time O(number of generations**(2.0-3.0)) and memory O(number of generations**(1.0-2.0)).",

notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}",