Pareto-Dominance Based MOGP for Evolving Soccer Agents

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Christopher Lazarus",
  booktitle =    "IEEE Symposium Series on Computational Intelligence",
  title =        "Pareto-Dominance Based MOGP for Evolving Soccer
  year =         "2015",
  pages =        "280--287",
  abstract =     "Robot behaviour generation is an attractive option to
                 automatically produce robot controllers. Most
                 high-level robot behaviours comprise multiple
                 objectives that may be conflicting with each other.
                 This research describes experiments using two
                 Pareto-dominance based algorithms together with a
                 Multiobjective Genetic Programming (MOGP) framework to
                 evolve high-level robot behaviours using only primitive
                 commands. The performance of hand-coded controllers are
                 compared against controllers evolved using the
                 Non-dominated Sorting Genetic Algorithm II (NSGA-II)
                 and Strength Pareto Evolutionary Algorithm 2 (SPEA2)
                 algorithms. An additional comparison is also performed
                 against controllers evolved using the weighted sum
                 fitness function. The experiment results show that the
                 Pareto dominance based MOGP performed better than the
                 hand-coded and the weighted sum evolved controllers.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2015.49",
  month =        dec,
  notes =        "Also known as \cite{7376622}",

Genetic Programming entries for Christopher Lazarus