Case study: constraint handling in evolutionary optimization of catalytic materials

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Martin Holena and David Linke and Lukas Bajer",
  title =        "Case study: constraint handling in evolutionary
                 optimization of catalytic materials",
  booktitle =    "GECCO 2011 Evolutionary computation techniques for
                 constraint handling",
  year =         "2011",
  editor =       "Carlos Artemio Coello Coello and Dara Curran and 
                 Thomas Jansen",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "333--340",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002015",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The paper presents a case study in an industrially
                 important application domain the optimization of
                 catalytic materials. Though evolutionary algorithms are
                 the by far most frequent approach to optimization tasks
                 in that domain, they are challenged by mixing
                 continuous and discrete variables, and especially by a
                 large number of constraints. The paper describes the
                 various kinds of encountered constraints, and explains
                 constraint handling in GENACAT, one of evolutionary
                 optimization systems developed specifically for
                 catalyst optimization. In particular, it is shown that
                 the interplay between cardinality constraints and
                 linear equality and inequality constraints allows
                 GENACAT to efficienlty determine the set of feasible
                 solutions, and to split the original optimization task
                 into a sequence of discrete and continuous
                 optimization. Finally, the genetic operations employed
                 in the discrete optimization are sketched, among which
                 crossover is based on an assumption about the
                 importance of the choice of sets of continuous
                 variables in the cardinality constraints.",
  notes =        "Also known as \cite{2002015} Distributed on CD-ROM at

                 ACM Order Number 910112.",

Genetic Programming entries for Martin Holena David Linke Lukas Bajer