Robust GP in Robot Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{hondo:1996:rGPrl,

• author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
• title = "Robust GP in Robot Learning",
• booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation",
• year = "1996",
• editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel",
• series = "LNCS",
• volume = "1141",
• pages = "751--760",
• address = "Berlin, Germany",
• publisher_address = "Heidelberg, Germany",
• month = "22-26 " # sep,
• publisher = "Springer Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-61723-X",
• DOI = "doi:10.1007/3-540-61723-X_1038",
• size = "10 pages",
• abstract = "This paper presents a new approach to Genetic Programming (i.e. GP). Our goal is to realise robustness by means of the automatic discovery of functions. In traditional GP, techniques have been proposed which attempt to discover certain subroutines for the sake of improved efficiency. So far, however, the robustness of GP has not yet been discussed in terms of knowledge acquisition. We propose an approach for robustness named COAST, which has a library for storing certain subroutines for reuse. We make use of the Wall Following Problem to illustrate the efficiency of this method.",
• notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 COAST, Wall following problem",
• affiliation = "Hokkaido University Complex Systems Engineering, Division of Systems and Information Engineering N-13 W-8, Sapporo 060 Hokkaido Japan N-13 W-8, Sapporo 060 Hokkaido Japan",

}

Genetic Programming entries for Naohiro Hondo Hitoshi Iba Yukinori Kakazu

Citations