Evolving Modularity in Robot Behaviour Using Gene Expression Programming

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

@InProceedings{conf/taros/MwauraK11,

• author = "Jonathan Mwaura and Ed Keedwell",
• title = "Evolving Modularity in Robot Behaviour Using Gene Expression Programming",
• booktitle = "Proceedings 12th Annual Conference Towards Autonomous Robotic Systems, (TAROS 2011)",
• year = "2011",
• editor = "Roderich Gross and Lyuba Alboul and Chris Melhuish and Mark Witkowski and Tony J. Prescott and Jacques Penders",
• volume = "6856",
• series = "Lecture Notes in Computer Science",
• pages = "392--393",
• address = "Sheffield, {UK}",
• month = aug # " 31-" # sep # " 2",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• isbn13 = "978-3-642-23231-2",
• DOI = "doi:10.1007/978-3-642-23232-9_43",
• size = "2 pages",
• abstract = "Incremental learning [3] and layered learning [4] have been proposed as suitable approaches to improve evolutionary robotic (ER) algorithms by subdividing the required behaviour into simpler tasks. However, incremental learning does not divide the controller to unique task modules and although layered learning subdivides the problem into modules it does not offer continuous learning for the various sub-behaviours. Moreover, both methods involve the modification of the fitness function in every module thus increasing computational overhead.",
• notes = "ugGEP mgGEP Lazarus room 2-5",
• affiliation = "College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK",
• bibdate = "2011-08-26",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/taros/taros2011.html#MwauraK11",

}

Genetic Programming entries for Jonathan Mwaura Ed Keedwell

Citations