Search Results for "binary differential evolution" - Page 2

Keywords: -simultaneously fit several SAXS and SANS data sets with polydisperse (Schultz-Zimm or Gaussian distribution f(R)) spherical core-shell-shell nanoparticles -analytical expressions are used for from factor F(Q) and its integral over f(R), no numerical integration required -absolute units -Mathematica is required via console (MathKernel) -Mathematica's local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters are possible -Monodisperse(!) hard sphere structure factor can be used, too -long computation times (depending on problem size and amount of constraints) from hours to a few days are possible -non-parallelized code

Keywords (XNDiff): -SAXS -SANS -absolute units -core (double)shell crystalline nanoparticles -with a parallelepidal shape -particle assemblies -powder and ensemble average -C/C++ -Unix -OpenMP -HPC Cluster Keywords (BatchMultiFit): -simultaneous fits for several SAXS and SANS curves with simulation data from XNDiff -SANS data can be smeared with dq values from experimental data sets or analytical functions -Mathematica console -local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters -parallelized (typ. 4-8 threads) TODO (BatchMultiFit): -read and use errorbars from experimental data sets -allow different q-ranges for different data sets in the fits -rewrite and test in Python using e.g. the lmfit module: https://pypi.python.org/pypi/lmfit/ to get rid of Mathematica and to run it on HPC clusters

Ofeli (as an acronym for Open, Fast and Efficient Level set Implementation) demonstrates how to operate an image segmentation algorithm of Y. Shi and W. C. Karl |1|, using a discrete approach for the approximation of level-set based curve evolution (implicit active contours). This is a novel (2005) and fast algorithm without the need of solving partial differential equations (PDE) while preserving the advantages of level set methods, such as the automatic handling of topological changes. Considerable speedups (×100) have been demonstrated as compared to PDE-based narrow band level-set implementations.

The DetStoch package offers the user two different methods to compute chemical reactions: Deterministic and Stochastic approaches. It allows the user to play with an intuitive GUI framework and compute the evolution rates of the chemical reactions with the different methods available. The results are displayed in graphical plots of the species concentration over the time. The package is designed to solve any of the following reactions by either one of the two approaches...

This is a GUI, Java implementation of the Ant Colony Optimisation/Particle Swarm Optimisation (PSO/ACO2) rule induction algorithm. This project was inspired by Ant-Miner, but handles continuous attributes using PSO or now Differential Evolution.

...AEON can be easily configured using text files to use different encodings and/or EAs on the same experiment. The framework currently supports standard EAs (Simple GA, Steady-State GA, etc.), Differential Evolution and a number of direct and indirect encodings (bitstring, real-valued, AGE, etc.).

Binary package of Ximian (Novell) Evolution (X11 version) for Mac OS X, with native Mac OS X installer.