Genetic algorithms in wavelength selection: a comparative study

Abstract This paper presents a comparative study involving a genetic algorithm, simulated annealing, and stepwise elimination, as methods for wavelength selection in multi-component analysis. The wavelength selection criteria used are the selectivity and accuracy after Lorber, and the minimal mean squared error after Sasaki. The genetic algorithm generally performed best. Stepwise elimination performed surprisingly good despite its local search heuristic. Simulated annealing performed worst, which is remarkable in view of the fact that this method is widely praised in the literature for properties similar to those of genetic algorithms, e.g., a probabilistic, non-local search heuristic.

[1]  Gerrit Kateman,et al.  Optimization of calibration data with the dynamic genetic algorithm , 1992 .

[2]  J. Kalivas.  A Simplex Optimized Inductively Coupled Plasma Spectrometer with Minimization of Interferences , 1987 .

[3]  John H. Kalivas.  Optimization using variations of simulated annealing , 1992 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  R. Ruthen.  The Frustrations of a Quark Hunter , 1992 .

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[8]  Satoshi Kawata,et al.  Optimal Wavelength Selection for Quantitative Analysis , 1986 .

[9]  C. B. Lucasius,et al.  On k-medoid clustering of large data sets with the aid of a genetic algorithm: background, feasiblity and comparison , 1993 .

[10]  C. B. Lucasius,et al.  Genetic algorithms for large-scale optimization in chemometrics: An application , 1991 .

[11]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 1. Concepts, properties and context , 1993 .

[12]  John H. Kalivas,et al.  Generalized simulated annealing for calibration sample selection from an existing set and orthogonalization of undesigned experiments , 1991 .

[13]  J. Kalivas,et al.  Evaluation of experimental designs for multicomponent determinations by spectrophotometry , 1988 .

[14]  A. Lorber.  Error propagation and figures of merit for quantification by solving matrix equations , 1986 .

[15]  John H. Kalivas,et al.  Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry , 1989 .

[16]  Kenneth R. Beebe,et al.  Selection of adsorbates for chemical sensor arrays by pattern recognition , 1986 .

[17]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

[18]  F. P. Zscheile,et al.  Instability of Linear Systems Derived from Spectrophotometric Analysis of Multicomponent Systems. , 1962 .

[19]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .

[20]  Edmund R. Malinowski,et al.  Obtaining the key set of typical vectors by factor analysis and subsequent isolation of component spectra , 1982 .