Peer Reviewed: Learning Optimization From Nature: Genetic Algorithms and Simulated Annealing

Numerical optimization techniques can be used to quantify traditional trial-and-error learning methods

[1]  C. B. Lucasius,et al.  Genetic algorithms in wavelength selection: a comparative study , 1994 .

[2]  M A Arnold,et al.  Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. , 1996, Analytical chemistry.

[3]  P. Derreumaux,et al.  Force-field and vibrational spectra of oligosaccharides with different glycosidic linkages—Part I. Trehalose dihydrate, sophorose monohydrate and laminaribiose , 1994 .

[4]  John H. Kalivas,et al.  Further investigation on a comparative study of simulated annealing and genetic algorithm for wavelength selection , 1995 .

[5]  Gary W. Small,et al.  Comparison of optimization algorithms for piecewise linear discriminant analysis: application to Fourier transform infrared remote sensing measurements , 1996 .

[6]  Automated simplex optimization for monochromatic imaging inductively coupled plasma atomic emission spectroscopy , 1995 .

[7]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[8]  A. Ferry,et al.  Curve Fitting and Deconvolution of Instrumental Broadening: A Simulated Annealing Approach , 1995 .

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

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

[11]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 2. Representation, configuration and hybridization , 1994 .

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