Representation and Structural Biases in CGP

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Payne:2009:cec,

• author = "Andrew J. Payne and Susan Stepney",
• title = "Representation and Structural Biases in CGP",
• booktitle = "2009 IEEE Congress on Evolutionary Computation",
• year = "2009",
• editor = "Andy Tyrrell",
• pages = "1064--1071",
• address = "Trondheim, Norway",
• month = "18-21 " # may,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-2959-2",
• file = "P019.pdf",
• DOI = "doi:10.1109/CEC.2009.4983064",
• abstract = "An evolutionary algorithm automatically discovers suitable solutions to a problem, which may lie anywhere in a large search space of candidate solutions. In the case of Genetic Programming, this means performing an efficient search of all possible computer programs represented as trees. Exploration of the search space appears to be constrained by structural mechanisms that exist in Genetic Programming as a consequence of using trees to represent solutions. As a result, programs with certain structures are more likely to be evolved, and others extremely unlikely. We investigate whether the graph representation used in Cartesian Genetic Programming causes an analogous biasing effect, imposing natural limitations on the class of solution structures that are likely to be evolved. Representation bias and structural bias are identified: the rarer {"}regular{"} structures appear to be easier to evolve than more common {"}irregular{"} ones.",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR",

}

Genetic Programming entries for Andrew J Payne Susan Stepney

Citations