Matching stochastic algorithms to objective function landscapes

W. P. Baritompa; M. Dür; E. M T Hendrix; L. Noakes; W. J. Pullan; G. R. Wood

doi:10.1007/s10898-004-9968-y

Matching stochastic algorithms to objective function landscapes

W. P. Baritompa^*, M. Dür, E. M T Hendrix, L. Noakes, W. J. Pullan, G. R. Wood

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

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.

Original language	English
Pages (from-to)	579-598
Number of pages	20
Journal	Journal of Global Optimization
Volume	31
Issue number	4
DOIs	https://doi.org/10.1007/s10898-004-9968-y
Publication status	Published - Apr 2005

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Matching stochastic algorithms to objective function landscapes

Abstract

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