Simple function forms and nucleon-nucleus total cross sections

Total cross sections for neutron scattering with energies between 10 and 600 MeV and from nine nuclei spanning the mass range from 6Li to 238U have been analyzed using a simple function of three parameters. The values of those parameters with which neutron total cross section data are replicated vary smoothly with energy and target mass and may themselves be represented by functions of energy and mass.Comment: 15 pages, 9 figure

A simple functional form for proton-208{}^{208}Pb total reaction cross sections

A simple functional form has been found that gives a good representation of the total reaction cross sections for the scattering from ${}^{208}$Pb of protons with energies in the range 30 to 300 MeV.Comment: 7 pages, 2 figure

A simple functional form for proton-nucleus total reaction cross sections

A simple functional form has been found that gives a good representation of the total reaction cross sections for the scattering of protons from (15) nuclei spanning the mass range ${}^{9}$Be to ${}^{238}$U and for proton energies ranging from 20 to 300 MeV.Comment: 13 pages, 7 figures, bib fil

Simple function form for n+208Pb total cross section between 5 and 600 MeV

The total cross section for neutron scattering from 208Pb with energies between 5 and 600 MeV has been analyzed extending a previously defined simple function of three parameters to reveal a Ramsauer-like effect throughout the whole energy range. This effect can be parametrized in a simple way so that it may be anticipated that the complete function prescription will apply for total cross sections from other nuclei.Comment: 9 pages, 4 firgure

Computationally effective search and optimization procedure using coarse to fine approximations

This paper presents a concept of combining genetic algorithms (GAs) with an approximate evaluation technique to achieve a computationally effective search and optimization procedure. The major objective of this work is to enable the use of GAs on computationally expensive problems, while retaining their basic robust search capabilities. Starting with a coarse approximation model of the problems, GAs successively use finer models, thereby allowing the proposed algorithm to find the optimal or a near-optimal solution of computationally expensive problems faster. A general methodology is proposed for combining any approximating technique with GA. The proposed methodology is also tested in conjunction with one particular approximating technique, namely the artificial neural network, on a B-spline curve fitting problem successfully. Savings in the exact function evaluation up to 32% are achieved. The computational advantage demonstrated here should encourage the use of the proposed approach to more complex and computationally demanding real-world problems