A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms

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

@InProceedings{eurogp07:hemberg,

• author = "Erik Hemberg and Conor Gilligan and Michael O'Neill and Anthony Brabazon",
• title = "A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms",
• editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
• booktitle = "Proceedings of the 10th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "4445",
• year = "2007",
• address = "Valencia, Spain",
• month = "11-13 " # apr,
• pages = "1--11",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-71602-5",
• isbn13 = "978-3-540-71602-0",
• DOI = "doi:10.1007/978-3-540-71605-1_1",
• abstract = "The ability of Genetic Programming to scale to problems of increasing difficulty operates on the premise that it is possible to capture regularities that exist in a problem environment by decomposition of the problem into a hierarchy of modules. As computer scientists and more generally as humans we tend to adopt a similar divide-and-conquer strategy in our problem solving. In this paper we consider the adoption of such a strategy for Genetic Algorithms. By adopting a modular representation in a Genetic Algorithm we can make efficiency gains that enable superior scaling characteristics to problems of increasing size. We present a comparison of two modular Genetic Algorithms, one of which is a Grammatical Genetic Programming algorithm, the meta-Grammar Genetic Algorithm (mGGA), which generates binary string sentences instead of traditional GP trees. A number of problems instances are tackled which extend the Checkerboard problem by introducing different kinds of regularity and noise. The results demonstrate some limitations of the modular GA (MGA) representation and how the mGGA can overcome these. The mGGA shows improved scaling when compared the MGA.",
• notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007",

}

Genetic Programming entries for Erik Hemberg Conor Gilligan Michael O'Neill Anthony Brabazon

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