Differential evolution - an easy and efficient evolutionary algorithm for model optimisation

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

@Article{Mayer:2005:AS,
  author =       "D. G. Mayer and B. P. Kinghorn and A. A. Archer",
  title =        "Differential evolution - an easy and efficient
                 evolutionary algorithm for model optimisation",
  journal =      "Agricultural Systems",
  year =         "2005",
  volume =       "83",
  pages =        "315--328",
  number =       "3",
  abstract =     "Recently, evolutionary algorithms (encompassing
                 genetic algorithms, evolution strategies, and genetic
                 programming) have proven to be the best general method
                 for the optimisation of large, difficult problems,
                 including agricultural models. Differential evolution
                 (DE) is one comparatively simple variant of an
                 evolutionary algorithm. DE has only three or four
                 operational parameters, and can be coded in about 20
                 lines of pseudo-code. Investigations of its performance
                 in the optimisation of a challenging beef property
                 model with 70 interacting management options (hence a
                 70-dimensional optimisation problem) indicate that DE
                 performs better than Genial (a real-value genetic
                 algorithm), which has been the preferred operational
                 package thus far. Despite DE's apparent simplicity, the
                 interacting key evolutionary operators of mutation and
                 recombination are present and effective. In particular,
                 DE has the advantage of incorporating a relatively
                 simple and efficient form of self-adapting mutation.
                 This is one of the main advantages found in evolution
                 strategies, but these methods usually require the
                 burdening overhead of doubling the dimensionality of
                 the search-space to achieve this. DE's processes are
                 illustrated, and model optimisations totalling over two
                 years of Sun workstation computation are presented.
                 These results show that the baseline DE parameters work
                 effectively, but can be improved in two ways. Firstly,
                 the population size does not need to be overly high,
                 and smaller populations can be considerably more
                 efficient; and second, the periodic application of
                 extrapolative mutation may be effective in
                 counteracting the contractive nature of DE's
                 intermediate arithmetic recombination in the latter
                 stages of the optimisations. This provides an escape
                 mechanism to prevent sub-optimal convergence. With its
                 ease of implementation and proven efficiency, DE is
                 ideally suited to both novice and experienced users
                 wishing to optimise their simulation models.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6T3W-4CWSVR5-1/2/d9e644ff5e8d53cade196bda234702bf",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Differential
                 evolution, Optimisation, FORTRAN, Beef model",
  DOI =          "doi:10.1016/j.agsy.2004.05.002",
}

Genetic Programming entries for David G Mayer B P Kinghorn A A Archer

Citations