Free encryption algorithm software for linux

Fast Genetic Algorithm is a simple yet powerful implementation of a general genetic algorithm, and provides many types of crossover and selection procedures.
It is suitable to solve mathematical problems such as combinatorical optimization ones, as well as to build artificial life simulations.
Written in C++, the library is released under the terms of the GNU Lesser General Public License, and its easy to incorporate in other applications.
Further improvements include parallelization of the algorithm in multi-processor environments and general performance optimizations.
You can download the current development snapshot, which is a working but not fully tested version of the library.
Enhancements:
- Dynamic arrays were fixed to compile even on non-C99 compilers (like MSVC).
- A switch to choose whether to preserve the fittest individual across generations was added.
- The pthreads-win32 library was included in the package, as well as Visual C++ and Dev-C++ projects to easily build the source code on Windows.
- A function that computes the standard deviation of fitness values was added (thanks to Jonas Neubert for the contribution).

Optimization Algorithm Toolkit is a workbench and toolkit for developing, evaluating, and playing with classical and state-of-the-art optimization algorithms on standard benchmark problem domains; including reference algorithm implementations, graphing, visualizations and much more. The project was developed initially by Jason Brownlee as a part of his Ph.D. program.
The goal of this project is to deliver obscure-and-prevalent, old-and-new optimization algorithms from research literature to both research scientists and algorithm practitioners alike. Algorithms include biologically inspired approaches such as evolutionary algorithms (genetic algorithms), swarm algorithms (ants and particle swarm), and immune system algorithms.
Also included are more conventional approaches such as approaches inspired by physics including simulated annealing and extremal optimization. Problem domains include numerical function optimization, traveling salesman problems, and protein folding all with many standard benchmark instances taken from research literature.
A user-friendly graphical interface is provided to rapidly evaluate and compare algorithm and problem configurations, visualize algorithm behavior, and graph algorithm performance over time. A robust, modular, and extensible framework underlies the platform to facilitate the easy addition and modification of algorithms, addition of new problem domains and problem instances as well as facilitate more advanced algorithm experimentation.
The algorithm implementations are extensible and easily support modification and applicaition to varied problem domains. Please report any bugs, feature requests or include your own algorithms by accessing the services on the project home website. This is an open source project (released under the GPL) so the source code is available. The project was compiled with Java 1.5 (update 9).
Enhancements:
- This release includes many framework and graphical interface fixes, as well as a few new algorithms to play with.

Gaim-Encryption uses NSS to provide transparent RSA encryption as a Gaim plugin.
Main features:
- Automatically creates a public/private key pair for you upon loading the plugin
- Automatically transmits your public key to other users.
- Supports 512 - 4096 bit keys.
- Saves keys of known users, and warns you if their public key has changed.
- Embeds all encryption and keys inside HTML, so if the other user doesnt have the plugin, they will get a little message telling them about the plugin, and wont get a screen full of garbage.
- Stores keys in human readable files in your .gaim directory, in case you ever need to copy/edit them by hand. Which you really shouldnt have to do.
- Automatically recognizes if you are chatting with someone who has the plugin- see the Preferences dialog.
- Available for the Windows version of Gaim.
- Modular and extensible. If you want to define a different type of encryption, you can use this plugin as a wrapper to take care of transporting the encrypted binary over the IM pipe.

mcl-algorithm is a scalable cluster algorithm for graphs based on stochastic flow.
The flow process employed by the algorithm is mathematically sound and intrinsically tied to cluster structure in graphs, which is revealed as the imprint left by the process.
The threaded implementation has handled graphs of up to one million nodes within hours, and is widely used in the field of protein family analysis. It comes with a wide range of sibling utilities for handling and analyzing graphs, matrices, and clusterings.
The MCL algorithm simulates flow using (alternating) two simple algebraic operations on matrices. Its formulation is simple and elegant. There are no high-level procedural instructions for assembling, joining, or splitting of groups - cluster structure is bootstrapped via a flow process that is inherently affected by any cluster structure present.
The first operation used by MCL is expansion, which coincides with normal matrix multiplication. Expansion models the spreading out of flow, it becoming more homogeneous. The second is inflation, which is mathematically speaking a Hadamard power followed by a diagonal scaling. Inflation models the contraction of flow, it becoming thicker in regions of higher current and thinner in regions of lower current. The MCL process causes flow to spread out within natural clusters and evaporate inbetween different clusters.
- By varying parameters, clusterings on different scales of granularity can be found. The number of clusters can not and need not be specified in advance, but the algorithm can be adapted to different contexts.
- The issue how many clusters? is not dealt with in an arbitrary manner, but rather by strong internal logic. Cluster structure leaves its marks on the flow process simulated by the algorithm, and the flow parameters control the granularity of the cluster imprint.
- The limit of the MCL process (the process simulated by the algorithm) is in general extremely sparse, and the iterands are sparse in a weighted sense. This gives the means to scale the algorithm drastically, leading to a worst-case complexity of order Nk^2, where N is the number of nodes of the input graph, and where k is a threshold for the number of resources allocated per node.
- The rate of convergence of the MCL process, and projection of the iterands afterwards onto the resulting clustering, give hooks for unsupervised parameter adjustment.
- The iterands of the MCL process have structural properties which allow a cluster interpretation, and which generalize the mapping of MCL limits onto clusterings. The mathematics associated with the MCL process shows that there is an intrinsic relationship between the MCL process and cluster structure in graphs. This is very valuable given the many heuristic approaches in cluster analysis.
Enhancements:
- Numerous cleanups in much of the code.
- Improvements in caching intermediate results.

Algorithm::C3 is a module for merging hierarchies using the C3 algorithm.

SYNOPSIS

use Algorithm::C3;

# merging a classic diamond
# inheritence graph like this:
#
#
# /
#
# /
#

my @merged = Algorithm::C3::merge(
D,
sub {
# extract the ISA array
# from the package
no strict refs;
@{$_[0] . ::ISA};
}
);

print join ", " => @merged; # prints D, B, C, A

This module implements the C3 algorithm. I have broken this out into its own module because I found myself copying and pasting it way too often for various needs. Most of the uses I have for C3 revolve around class building and metamodels, but it could also be used for things like dependency resolution as well since it tends to do such a nice job of preserving local precendence orderings.