- author = "Marc-Andre Gardner and Christian Gagne and Marc Parizeau",
- title = "Bloat control in genetic programming with a histogram-based accept-reject method",
- booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation",
- year = "2011",
- editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming: Poster",
- pages = "187--188",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2001963",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- size = "2 pages",
- abstract = "Recent bloat control methods such as dynamic depth limit (DynLimit) and Dynamic Operator Equalisation (DynOpEq) aim at modifying the tree size distribution in a population of genetic programs. Although they are quite efficient for that purpose, these techniques have the disadvantage of evaluating the fitness of many bloated Genetic Programming (GP) trees, and then rejecting most of them, leading to an important waste of computational resources. We are proposing a method that makes a histogram-based model of current GP tree size distribution, and uses the so-called accept-reject method for generating a population with the desired target size distribution, in order to make a stochastic control of bloat in the course of the evolution. Experimental results show that the method is able to control bloat as well as other state-of-the-art methods, with minimal additional computational efforts compared to standard tree-based GP.",
-
notes = "symbolic regression, Santa Fe Ant, 6 parity. Like
operator equalisation?? but does not need to evaluate
fitness before deciding if child fits into desired
distribution of program sizes. Cut off wrong word.
Above target allow size histogram falls exponentially.
Does not seem to limit small programs. Seem to be
missing point about distribution of sizes actually
generated by crossover. HARM-GP
deap.googlecode.com
Also known as \cite{2001963} Distributed on CD-ROM at GECCO-2011.
ACM Order Number 910112.",
