Genetic Algorithms Optimization for High Temperature Superconductors SN Class Molecular Effect Model with Electronics Applications

Abstract

This contribution deals with the Molecular Effect Model (MEM) Genetic Algorithms polynomial-dual optimization for High Temperature Superconductors (HTSCs) class of [ Sn-Sb-Te-Ba-Mn-Cu-O ] . Results comprise Tikhonov Regularization Functionals development and mathematical methods for this HTSCs group without using logarithmic changes. Findings for this MEM optimization, based on Genetic Algorithms polynomial-dual-method show acceptable theoretical Numerical and 2D/3D Graphical Optimization solutions and low residuals. Solutions comprise two parts, the modelling for TC Molecular Effect predictions equations, and 2D graphics series of results. Electronics Physics applications for Superconductors and High Temperature Superconductors are specified for Isotope Effect in BCS theory and for  MEM and presented.