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Matching Stochastic Algorithms to Objective Function Landscapes |
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| Authors: | W?P?Baritompa M?Dür E?M?T?Hendrix L?Noakes W?J?Pullan |
| Institution: | (1) Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand;(2) Department of Mathematics, Darmstadt University of Technology, D-64289 Darmstadt, Germany;(3) Group Operations Research and Logistics, Wageningen University, Hollandseweg 1, 6706, KN, Wageningen, The Netherlands;(4) School of Mathematics and Statistics, University of Western Australia, Nedlands, WA, 6907, Australia;(5) School of Information Technology, Griffith University, Gold Coast, Australia;(6) Department of Statistics, Macquarie University, North Ryde, NSW, 2109, Australia |
| Abstract: | Large scale optimisation problems are frequently solved using stochastic methods. Such methods often generate points randomly in a search region in a neighbourhood of the current point, backtrack to get past barriers and employ a local optimiser. The aim of this paper is to explore how these algorithmic components should be used, given a particular objective function landscape. In a nutshell, we begin to provide rules for efficient travel, if we have some knowledge of the large or small scale geometry. |
| Keywords: | Backtracking Global optimisation Local optimisation Search region Simulated annealing Temperature |
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