Constituent Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Georgiou:2011:IJCAI,

• author = "Loukas Georgiou and William J. Teahan",
• title = "Constituent Grammatical Evolution",
• booktitle = "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence",
• year = "2011",
• editor = "Toby Walsh",
• pages = "1261--1268",
• address = "Barcelona, Spain",
• publisher_address = "Menlo Park, California, USA",
• month = "16-22 " # jul,
• organisation = "International Joint Conferences on Artificial Intelligence",
• publisher = "AAAI Press",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Santa Fe Trail",
• isbn13 = "978-1-57735-512-0",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.1709",
• URL = "http://ijcai.org/papers11/Papers/IJCAI11-214.pdf",
• size = "8 pages",
• abstract = "We present Constituent Grammatical Evolution (CGE), a new evolutionary automatic programming algorithm that extends the standard Grammatical Evolution algorithm by incorporating the concepts of constituent genes and conditional behaviour-switching. CGE builds from elementary and more complex building blocks a control program which dictates the behaviour of an agent and it is applicable to the class of problems where the subject of search is the behaviour of an agent in a given environment. It takes advantage of the powerful Grammatical Evolution feature of using a BNF grammar definition as a plug-in component to describe the output language to be produced by the system. The main benchmark problem in which CGE is evaluated is the Santa Fe Trail problem using a BNF grammar definition which defines a search space semantically equivalent with that of the original definition of the problem by Koza. Furthermore, CGE is evaluated on two additional problems, the Los Altos Hills and the Hampton Court Maze. The experimental results demonstrate that Constituent Grammatical Evolution outperforms the standard Grammatical Evolution algorithm in these problems, in terms of both efficiency (percent of solutions found) and effectiveness (number of required steps of solutions found).",
• notes = "Santa Fe Ant, Lost Altos Hills, Hampton Court Maze, jGE http://ijcai.org/papers11/contents.php",

}

Genetic Programming entries for Loukas Georgiou William J Teahan

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