Matching Stochastic Algorithms to Objective Function Landscapes
Author(s)
Baritompa, WP
Dur, M
Hendrix, EMT
Noakes, L
Pullan, WJ
Wood, GR
Griffith University Author(s)
Year published
2005
Metadata
Show full item recordAbstract
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.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.
View less >
View less >
Journal Title
Journal of Global Optimization
Volume
31
Issue
4
Subject
Applied mathematics
Numerical and computational mathematics
Theory of computation