Search Results for "binary differential evolution"

The Differential Evolution, introduced in 1995 by Storn and Price, considers the population, that is divided into branches, one per computational node. The Differential Evolution Entirely Parallel method takes into account the individual age, that is defined as the number of iterations the individual survived without changes. The introduced improvements are: (I) allow several oldest individuals to be overwritten by the same number of best ones in the population, (II) new selection rule uses several objective functions in offspring evaluation. ...

©XSIBackup-DC is a C binary that runs in the ©ESXi shell and behaves as a backup and replica service. It follows the same principles as our previous ©XSIBackup editions, that is: a utility that runs in the hypervisor command line and accepts arguments and values stating which VMs to backup and where to copy them. It accepts local and remote SSH paths, thus you can backup and replicate VMs to local datastores or to remote repositories in any additional ©ESXi or Linux server. Download...

The ASCO project aims to bring circuit optimization capabilities to existing SPICE simulators using a high-performance parallel differential evolution (DE) optimization algorithm. It supports Eldo, HSPICE, LTspice, Spectre, and Qucs.

AFL (American Fuzzy Lop) is a widely used graybox fuzzer that discovers bugs by mutating inputs and steering execution using lightweight instrumentation. Instead of random mutations alone, it uses coverage feedback to evolve input corpora, pushing programs into deeper and more interesting code paths. Its workflow emphasizes quick start: point it at a target binary with compile-time instrumentation (or use QEMU-based mode when recompilation isn’t possible), seed it with a small corpus, and...

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