- author = "Steven M. Gustafson and William H. Hsu",
- title = "Layered Learning in Genetic Programming for a Co-operative Robot Soccer Problem",
- booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
- year = "2001",
- editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon",
- volume = "2038",
- series = "LNCS",
- pages = "291--301",
- address = "Lake Como, Italy",
- publisher_address = "Berlin",
- month = "18-20 " # apr,
- organisation = "EvoNET",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming, Layered Learning, Hierarchical abstractions, Robot soccer, Robots, Multiagent systems: Poster",
- ISBN = "3-540-41899-7",
-
URL = "
http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2001.ps",
-
URL = "
http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2001.pdf",
-
DOI = "
doi:10.1007/3-540-45355-5_23",
- size = "11 pages",
-
abstract = "We present an alternative to standard genetic
programming (GP) that applies layered learning
techniques to decompose a problem. GP is applied to
subproblems sequentially, where the population in the
last generation of a subproblem is used as the initial
population of the next subproblem. This method is
applied to evolve agents to play keep-away soccer, a
subproblem of robotic soccer that requires cooperation
among multiple agents in a dynnamic environment. The
layered learning paradigm allows GP to evolve better
solutions faster than standard GP. Results show that
the layered learning GP outperforms standard GP by
evolving a lower fitness faster and an overall better
fitness. Results indicate a wide area of future
research with layered learning in GP.
",
- notes = "EuroGP'2001, part of miller:2001:gp. See also \cite{gustafson:mastersthesis}",
