Login | DPI Staff queries on depositing or searching to era.daf.qld.gov.au

Survival of the fittest—genetic algorithms versus evolution strategies in the optimization of systems models

Share this record

Add to FacebookAdd to LinkedinAdd to XAdd to WechatAdd to Microsoft_teamsAdd to WhatsappAdd to Any

Export this record

View Altmetrics

Mayer, D.G., Belward, J.A., Widell, H. and Burrage, K. (1999) Survival of the fittest—genetic algorithms versus evolution strategies in the optimization of systems models. Agricultural Systems, 60 (2). pp. 113-122.

Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link.

Article Link: http://dx.doi.org/10.1016/S0308-521X(99)00022-0

Publisher URL: http://www.elsevier.com

Abstract

The use of numerical optimization techniques on simulation models is a developing field. Many of the available algorithms are not well suited to the types of problems posed by models of agricultural systems. Coming from different historical and developmental backgrounds, both genetic algorithms and evolution strategies have proven to be thorough and efficient methods in identifying the global optimum of such systems. A challenging herd dynamics model is used to test and compare optimizations using binary and real-value genetic algorithms, as well as evolution strategies. All proved successful in identifying the global optimum of this model, but evolution strategies were notably slower in achieving this. As the more successful innovations of each of these methods are being commonly adopted by all, the boundaries between them are becoming less clear-cut. They are effectively merging into one general class of optimization methods now termed evolutionary algorithms.

Item Type:Article
Corporate Creators:Animal Science
Additional Information:© Elsevier Science Ltd.
Keywords:Optimization; model; genetic algorithm; evolution strategy.
Subjects:Science > Statistics > Simulation modelling
Live Archive:09 Dec 2003
Last Modified:03 Sep 2021 16:46

Repository Staff Only: item control page