Evaluation of stochastic algorithm performance on antenna optimization benchmarks

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

  author =       "Irina Brinster and Philippe {De Wagter} and 
                 Jason Lohn",
  booktitle =    "Antennas and Propagation Society International
                 Symposium (APSURSI), 2012 IEEE",
  title =        "Evaluation of stochastic algorithm performance on
                 antenna optimization benchmarks",
  year =         "2012",
  isbn13 =       "978-1-4673-0461-0",
  address =      "Chicago, IL, USA",
  size =         "2 pages",
  abstract =     "This paper evaluates performance of ten stochastic
                 search algorithms on a benchmark suite of four antenna
                 optimisation problems. Hill climbers (HC) serve as
                 baseline algorithms. We implement several variants of
                 genetic algorithms, evolution strategies, and genetic
                 programming as examples of competitive strategy for
                 achieving optimal solution. Ant colony and
                 particle-swarm optimisation represent cooperative
                 strategy. Static performance is measured in terms of
                 success rates and mean hit time, while dynamic
                 performance is evaluated from the development of the
                 mean solution quality. Among the evaluated algorithms,
                 steady-state GA provides the best trade-off between
                 efficiency and effectiveness. PSO is recommended for
                 noisy problems, while ACO and GP should be avoided for
                 antenna optimisations because of their low
  keywords =     "genetic algorithms, genetic programming, ant colony
                 optimisation, antennas, particle swarm optimisation,
                 search problems, stochastic processes, Hill climbers,
                 ant colony optimisation, antenna optimisation
                 benchmark, cooperative strategy, evolution strategies,
                 particle swarm optimisation, steady-state GA,
                 stochastic algorithm performance, stochastic search
                 algorithm, Antennas, Arrays, Benchmark testing,
                 Electromagnetics, Heuristic algorithms, Optimisation",
  DOI =          "doi:10.1109/APS.2012.6348758",
  ISSN =         "1522-3965",
  notes =        "Also known as \cite{6348758}",

Genetic Programming entries for Irina Brinster Philippe De Wagter Jason Lohn