Evaluation of stochastic algorithm performance on antenna optimization benchmarks

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Brinster:2012:APSURSI,

• 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 efficiencies.",
• 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

Citations