๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Wavelength selection with Tabu Search

โœ Scribed by J. A. Hageman; M. Streppel; R. Wehrens; L. M. C. Buydens


Publisher
John Wiley and Sons
Year
2003
Tongue
English
Weight
274 KB
Volume
17
Category
Article
ISSN
0886-9383

No coin nor oath required. For personal study only.

โœฆ Synopsis


Abstract

This paper introduces Tabu Search in analytical chemistry by applying it to wavelength selection. Tabu Search is a deterministic global optimization technique loosely based on concepts from artificial intelligence. Wavelength selection is a method which can be used for improving the quality of calibration models. Tabu Search uses basic, problemโ€specific operators to explore a search space, and memory to keep track of parts already visited. Several implementational aspects of wavelength selection with Tabu Search will be discussed. Two ways of memorizing the search space are investigated: storing the actual solutions and storing the steps necessary to create them. Parameters associated with Tabu Search are configured with a Plackettโ€“Burman design. In addition, two extension schemes for Tabu Search, intensification and diversification, have been implemented and are applied with good results. Eventually, two implementations of wavelength selection with Tabu Search are tested, one which searches for a solution with a constant number of wavelengths and one with a variable number of wavelengths. Both implementations are compared with results obtained by wavelength selection methods based on simulated annealing (SA) and genetic algorithms (GAs). It is demonstrated with three realโ€world data sets that Tabu Search performs equally well as and can be a valuable alternative to SA and GAs. The improvements in predictive abilities increased by a factor of 20 for data set 1 and by a factor of 2 for data sets 2 and 3. In addition, when the number of wavelengths in a solution is variable, measurements on the coverage of the search space show that the coverage is usually higher for Tabu Search compared with SA and GAs. Copyright ยฉ 2003 John Wiley & Sons, Ltd.


๐Ÿ“œ SIMILAR VOLUMES


Feature selection using tabu search meth
โœ Hongbin Zhang; Guangyu Sun ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 236 KB

Selecting an optimal subset from original large feature set in the design of pattern classi"er is an important and di$cult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method, e

Optimal reference subset selection for n
โœ Hongbin Zhang; Guangyu Sun ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 145 KB

This paper presents an approach to select the optimal reference subset (ORS) for nearest neighbor classiรฟer. The optimal reference subset, which has minimum sample size and satisรฟes a certain resubstitution error rate threshold, is obtained through a tabu search (TS) algorithm. When the error rate t

Driving Tabu Search with case-based reas
โœ Stephan Grolimund; Jean-Gabriel Ganascia ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 997 KB

When it is important to solve hard optimisation problems efficiently, e.g. as in Decision Support Systems, meta-heuristics like Tabu Search often propose valuable alternatives in case exact optimisation is not available. Further, such techniques are in general flexible enough to adapt problem modell

A tabu search heuristic for the undirect
โœ Michel Gendreau; Gilbert Laporte; Frรฉdรฉric Semet ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 677 KB

The undirected Selective Travelling Salesman Problem (STSP) is defined on a graph G= ( V, E) with positive profits associated with vertices, and distances associated with edges. The STSP consists of determining a maximal profit Hamiltonian cycle over a subset of V whose length does not exceed a pres