Searching for Novel Regression Functions

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

@InProceedings{Martinez:2013:CEC,
  article_id =   "1619",
  author =       "Yuliana Martinez and Enrique Naredo and 
                 Leonardo Trujillo and Edgar Galvan-Lopez",
  title =        "Searching for Novel Regression Functions",
  booktitle =    "2013 IEEE Conference on Evolutionary Computation",
  volume =       "1",
  year =         "2013",
  month =        jun # " 20-23",
  editor =       "Luis Gerardo {de la Fraga}",
  pages =        "16--23",
  address =      "Cancun, Mexico",
  keywords =     "genetic algorithms, genetic programming, Novelty
                 Search, Behaviour-based Search, Symbolic Regression",
  isbn13 =       "978-1-4799-0453-2",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.5281",
  URL =          "http://eplex.cs.ucf.edu/noveltysearch/userspage/CEC-2013.pdf",
  DOI =          "doi:10.1109/CEC.2013.6557548",
  size =         "8 pages",
  abstract =     "The objective function is the core element in most
                 search algorithms that are used to solve engineering
                 and scientific problems, referred to as the fitness
                 function in evolutionary computation. Some researchers
                 have attempted to bridge this difference by reducing
                 the need for an explicit fitness function. A noteworthy
                 example is the novelty search (NS) algorithm, that
                 substitutes fitness with a measure of uniqueness, or
                 novelty, that each individual introduces into the
                 search. NS employs the concept of behavioural space,
                 where each individual is described by a domain-specific
                 descriptor that captures the main features of an
                 individuals performance. However, defining a behavioral
                 descriptor is not trivial, and most works with NS have
                 focused on robotics. This paper is an extension of
                 recent attempts to expand the application domain of NS.
                 In particular, it represents the first attempt to apply
                 NS on symbolic regression with genetic programming
                 (GP). The relationship between the proposed NS
                 algorithm and recent semantics-based GP algorithms is
                 explored. Results are encouraging and consistent with
                 recent findings, where NS achieves below average
                 performance on easy problems, and achieves very good
                 performance on hard problems. In summary, this paper
                 presents the first attempt to apply NS on symbolic
                 regression, a continuation of recent research devoted
                 at extending the domain of competence for
                 behaviour-based search.",
  notes =        "NS-GP-R coded in GPLAB MATLAB. Semantic
                 Similarity-based Crossover \cite{Quang:2011:GPEM}.

                 CEC 2013 - A joint meeting of the IEEE, the EPS and the
                 IET.",
}

Genetic Programming entries for Yuliana Martinez Enrique Naredo Leonardo Trujillo Edgar Galvan Lopez

Citations