Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems

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

@Article{Zhang:2009:ieeetASE,

• author = "Yang Zhang and Peter I. Rockett",
• title = "Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems",
• journal = "IEEE Transactions on Automation Science and Engineering",
• year = "2009",
• month = apr,
• volume = "6",
• number = "2",
• pages = "372--376",
• keywords = "genetic algorithms, genetic programming, classifiers, data-driven machine learning method, domain knowledge, domain-dependent feature extraction, multiobjective genetic programming, robot failure recognition systems, control engineering computing, feature extraction, learning (artificial intelligence), telerobotics",
• abstract = "We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge.",
• DOI = "doi:10.1109/TASE.2008.2004414",
• ISSN = "1545-5955",
• notes = "Also known as \cite{4667633}",

}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations