NEAT in HyperNEAT Substituted with Genetic Programming

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

@InProceedings{Buk:2009:ICANNGA,

• author = "Zdenek Buk and Jan Koutnik and Miroslav Snorek",
• title = "{NEAT} in {HyperNEAT} Substituted with Genetic Programming",
• year = "2009",
• booktitle = "9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009",
• editor = "Mikko Kolehmainen and Pekka Toivanen and Bartlomiej Beliczynski",
• series = "Lecture Notes in Computer Science",
• volume = "5495",
• pages = "243--252",
• address = "Kuopio, Finland",
• month = "23-25 " # apr,
• publisher = "Springer",
• note = "Revised selected papers",
• keywords = "genetic algorithms, genetic programming, HyperGP, ANN",
• isbn13 = "978-3-642-04920-0",
• DOI = "doi:10.1007/978-3-642-04921-7_25",
• size = "10 pages",
• abstract = "In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers with similar abilities.",
• notes = "ICANNGA 2009",

}

Genetic Programming entries for Zdenek Buk Jan Koutnik Miroslav Snorek

Citations