A New Multiobjective Genetic Programming for Extraction of Design Information from Non-dominated Solutions

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/emo/TatsukawaNOF13,
  author =       "Tomoaki Tatsukawa and Taku Nonomura and 
                 Akira Oyama and Kozo Fujii",
  title =        "A New Multiobjective Genetic Programming for
                 Extraction of Design Information from Non-dominated
                 Solutions",
  bibdate =      "2013-03-13",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/emo/emo2013.html#TatsukawaNOF13",
  booktitle =    "Evolutionary Multi-Criterion Optimization - 7th
                 International Conference, {EMO} 2013, Sheffield, {UK},
                 March 19-22, 2013. Proceedings",
  publisher =    "Springer",
  year =         "2013",
  volume =       "7811",
  editor =       "Robin C. Purshouse and Peter J. Fleming and 
                 Carlos M. Fonseca and Salvatore Greco and Jane Shaw",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37139-4",
  pages =        "528--542",
  series =       "Lecture Notes in Computer Science",
  URL =          "http://dx.doi.org/10.1007/978-3-642-37140-0",
  DOI =          "doi:10.1007/978-3-642-37140-0_40",
  abstract =     "We propose a new type of multi-objective genetic
                 programming (MOGP) for multi-objective design
                 exploration (MODE). The characteristic of the new MOGP
                 is the simultaneous symbolic regression to multiple
                 objective functions using correlation coefficients.
                 This methodology is applied to non-dominated solutions
                 of the multi-objective design optimisation problem to
                 extract information between objective functions and
                 design parameters. The result of MOGP is symbolic
                 equations that are highly correlated to each objective
                 function through a single GP run. These equations are
                 also highly correlated to several objective functions.
                 The results indicate that the proposed MOGP is capable
                 of finding new design parameters more closely related
                 to the objective functions than the original design
                 parameters. The proposed MOGP is applied to the test
                 problem and the practical design problem to evaluate
                 the capability.",
}

Genetic Programming entries for Tomoaki Tatsukawa Taku Nonomura Akira Oyama Kozo Fujii

Citations