Contribution in an anthology

Appeared in: Optimisation, Econometric and Financial Analysis

Editor list: Kontoghiorghes, E.J.; Gatu, C.

Publication year: 2007

Pages: 107-125

ISBN: 978-3-540-36625-6

eISBN: 978-3-540-36626-3

DOI Link: https://doi.org/10.1007/3-540-36626-1_6

Title of series: Advances in Computational Management Science

Number in series: 9

Abstract: 

Threshold Accepting (TA) is a powerful optimisation heuristic from the class of evolutionary algorithms. Using several examples from economics, econometrics and statistics, the issues related to implementations of TA are discussed and demonstrated. A problem specific implementation involves the definition of a local structure on the search space, the analysis of the objective function and of constraints, if relevant, and the generation of a sequence of threshold values to be used in the acceptance-rejection-step of the algorithm. A routine approach towards setting these implementation specific details for TA is presented, which will be partially data driven. Furthermore, fine tuning of parameters and the cost and benefit of restart versions of stochastic optimisation heuristics will be discussed.

Harvard Citation style: Winker, P. and Maringer, D. (2007) The Threshold Accepting Optimisation Algorithm in Economics and Statistics, in Kontoghiorghes, E. and Gatu, C. (eds.) Optimisation, Econometric and Financial Analysis. Berlin: Springer, pp. 107-125. https://doi.org/10.1007/3-540-36626-1_6

APA Citation style: Winker, P., & Maringer, D. (2007). The Threshold Accepting Optimisation Algorithm in Economics and Statistics. In Kontoghiorghes, E., & Gatu, C. (Eds.), Optimisation, Econometric and Financial Analysis (pp. 107-125). Springer. https://doi.org/10.1007/3-540-36626-1_6