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Smooth genetic algorithm
- Source :
- Journal of Physics A: Mathematical and General. 27:7893-7904
- Publication Year :
- 1994
- Publisher :
- IOP Publishing, 1994.
-
Abstract
- An existing family of genetic algorithms, which were designed with discrete and binary variables in mind, has been extended in this paper to handle truly continuous variables. Its close relationships with Monte Carlo methods, the simplex method, simulated annealing and other direct, i.e. Derivative-free global optimization algorithms creates a really versatile tool for various difficult optimization tasks. The main area of its application should be the reconstruction of unknown, continuous, and possibly smooth, distributions of various physical quantities derived from the experimental data. Among them might be: grain-size distribution for particulate magnetic materials derived from isothermal magnetization curves, distribution of relaxation times derived from luminescence experiments or chemical kinetics (inverse Laplace transform), and other large-scale numerically hard problems. One such problem, namely solving for the grain-size distribution for particulate magnetic materials, is presented as a working example and treated in detail. Applications of this algorithm should be stable deconvolution of various spectra with a variable window and non-parametric curve smoothing with a non-smooth objective function.
- Subjects :
- Mathematical optimization
Monte Carlo method
General Physics and Astronomy
Relaxation (iterative method)
Statistical and Nonlinear Physics
Inverse Laplace transform
Simplex algorithm
Simulated annealing
Genetic algorithm
Applied mathematics
Deconvolution
Global optimization
Mathematical Physics
Mathematics
Subjects
Details
- ISSN :
- 13616447 and 03054470
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- Journal of Physics A: Mathematical and General
- Accession number :
- edsair.doi...........8a0373261bf1a3a6a174d5c1365560f8