More Efficient Evolution of Small Genetic Programs in Cartesian Genetic Programming by Using Genotypic Age

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Kalkreuth:2016:cec,
  author =       "Roman Kalkreuth and Guenter Rudolph and Joerg Krone",
  title =        "More Efficient Evolution of Small Genetic Programs in
                 Cartesian Genetic Programming by Using Genotypic Age",
  booktitle =    "2016 IEEE Congress of Evolutionary Computation",
  year =         "2016",
  editor =       "Yun Li",
  pages =        "5052--5059",
  address =      "Vancouver",
  month =        "25-29 " # jul,
  publisher =    "IEEE",
  email =        "roman.kalkreuth@tu-dortmund.de",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7748330",
  abstract =     "Genetic Programming as an automated method to evolve
                 suitable computer programs for a predefined task can
                 also be applied to multi-objective optimization
                 problems. Originally, Genetic Programming uses tree
                 structures for the representation of a computer
                 program, but further development also enabled a graph
                 based representation called Cartesian Genetic
                 Programming. In the last years, Cartesian Genetic
                 Programming has also been applied to multi-objective
                 optimization problems. For example, we use this
                 representation to determine smaller mathematical
                 expressions or image processing filters with a maximum
                 number of operators. Previous research showed that
                 algorithm stagnation is a common issue in Cartesian
                 Genetic Programming. This behaviour comes along with a
                 decrease of diversity in the population and increases
                 the computational effort to find a suitable solution.
                 In this paper, we combine the multi-objective search
                 for smaller genetic programs with an efficient
                 diversity preservation technique. A modified version of
                 the popular NSGA-II algorithm is presented to evolve
                 small programs with a lower amount of fitness
                 evaluations and a higher success rate.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Roman Kalkreuth Guenter Rudolph Joerg Krone

Citations