A Dimensionally-Aware Genetic Programming Architecture for Automated Innovization

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

@InProceedings{conf/emo/BandaruD13,
  author =       "Sunith Bandaru and Kalyanmoy Deb",
  title =        "A Dimensionally-Aware Genetic Programming Architecture
                 for Automated Innovization",
  booktitle =    "Proceedings of the 7th International Conference on
                 Evolutionary Multi-Criterion Optimization, EMO 2013",
  year =         "2013",
  editor =       "Robin C. Purshouse and Peter J. Fleming and 
                 Carlos M. Fonseca and Salvatore Greco and Jane Shaw",
  volume =       "7811",
  series =       "Lecture Notes in Computer Science",
  pages =        "513--527",
  address =      "Sheffield, UK",
  month =        mar # " 19-22",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, dimensional
                 awareness, automated innovization, multi-objective
                 optimization, design principles",
  bibdate =      "2013-03-13",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/emo/emo2013.html#BandaruD13",
  isbn13 =       "978-3-642-37139-4",
  URL =          "http://dx.doi.org/10.1007/978-3-642-37140-0",
  DOI =          "doi:10.1007/978-3-642-37140-0_39",
  size =         "15 pages",
  abstract =     "Automated innovization is an unsupervised machine
                 learning technique for extracting useful design
                 knowledge from Pareto-optimal solutions in the form of
                 mathematical relationships of a certain structure.
                 These relationships are known as design principles.
                 Past studies have shown the applicability of automated
                 innovization on a number of engineering design
                 optimisation problems using a multiplicative form for
                 the design principles. In this paper, we generalise the
                 structure of the obtained principles using a tree-based
                 genetic programming framework. While the underlying
                 innovisation algorithm remains the same, evolving
                 multiple trees, each representing a different design
                 principle, is a challenging task. We also propose a
                 method for introducing dimensionality information in
                 the search process to produce design principles that
                 are not just empirical in nature, but also meaningful
                 to the user. The procedure is illustrated for three
                 engineering design problems.",
}

Genetic Programming entries for Sunith Bandaru Kalyanmoy Deb

Citations