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Traffic Control - Next Generation 10b
Traffic Control - Next Generation (tcng) is a revision of the Linux network traffic control infrastructure more>>
Traffic Control - Next Generation (tcng) is a revision of the Linux network traffic control infrastructure that aims to make the configuration language less cryptic, and provide better interfaces for software and hardware accelerators.
The goal of this project is to revise the network traffic control infrastructure of Linux to overcome shortcomings of the existing architecture, and to make it more extensible.
In particular, we try to resolve the following issues that are present in other similar programs:
Create a more user-friendly configuration language
Provide interfaces for straightforward interaction with network management
Allow seamless integration of hardware accelerators
The code consists of two major components, the traffic control compiler tcng and the traffic control simulator tcsim. Both are described in some more detail below. Since tcng and tcsim share many support files (e.g. the whole regression test system), they are both contained in a single package, called tcng.
This site contains the source code and information directly related to tcng. Pointers to sites with information on traffic control in general can be found in the links section below.
This project started at the beginning of 2001 at EPFL ICA, continued until mid-2002 at Bivio Networks (tm), and has now become one of my spare time activities.
Enhancements:
- the "mtu" parameter in TBF is now optional
- tcsim now uses KVERSION[NUM] instead of KFULLVERSION[NUM] to avoid breaking if EXTRAVERSION contains multiple dots or other surprises (reported by Eduardo Grosclaude)
- scripts/runtests.sh now runs commands with LANG=C, to avoid localized error messages (reported by Eduardo Grosclaude)
<<lessThe goal of this project is to revise the network traffic control infrastructure of Linux to overcome shortcomings of the existing architecture, and to make it more extensible.
In particular, we try to resolve the following issues that are present in other similar programs:
Create a more user-friendly configuration language
Provide interfaces for straightforward interaction with network management
Allow seamless integration of hardware accelerators
The code consists of two major components, the traffic control compiler tcng and the traffic control simulator tcsim. Both are described in some more detail below. Since tcng and tcsim share many support files (e.g. the whole regression test system), they are both contained in a single package, called tcng.
This site contains the source code and information directly related to tcng. Pointers to sites with information on traffic control in general can be found in the links section below.
This project started at the beginning of 2001 at EPFL ICA, continued until mid-2002 at Bivio Networks (tm), and has now become one of my spare time activities.
Enhancements:
- the "mtu" parameter in TBF is now optional
- tcsim now uses KVERSION[NUM] instead of KFULLVERSION[NUM] to avoid breaking if EXTRAVERSION contains multiple dots or other surprises (reported by Eduardo Grosclaude)
- scripts/runtests.sh now runs commands with LANG=C, to avoid localized error messages (reported by Eduardo Grosclaude)
Download (0.50MB)
Added: 2006-07-06 License: GPL (GNU General Public License) Price:
1212 downloads
Evolvica 0.6.3.2
Evolvica is a Java evolutionary computation framework. more>>
Evolvica is a Java Framework for evolutionary algorithms. It is the successor of eaLib, which has been developed at the Department of Electronic Circuits & Systems of the Technical University of Ilmenau, Germany from 2000 to 2002. The development on eaLib has been stopped and focuses now on Evolvica. However, eaLib is still available here.
There were quite some things in eaLib that were too complicated to be used. Therefore efforts have been undertaken to simplify the eaLib structure while preserving its flexibility. In addition the internal structure of genetic operators have changed in a way to support a creation of algorithms via a graphical user interface.
The goal for the future is to provide a complete development environment for evolutionary algorithms, including visual algorithm composer, Java editor, debugger and visual result analysis. This would be quite a lot of work but fortunately there is no need to invent the wheel for the 733rd time. Evolvica is based on the Eclipse Platform and uses the foundations provided by this great toolkit. All the Java features (source code editor, debugger and so on) originate from the Eclipse project.
Please note that the current state of the project is still classified as experimental. The version that you can download does not have production quality, it is a functional prototype that is still in some kind of an alpha-state. Several things are working, several others may not as expected. But you are free to try and find out for yourself and you are invited to share your experiences with others on the Evolvica user forum.
<<lessThere were quite some things in eaLib that were too complicated to be used. Therefore efforts have been undertaken to simplify the eaLib structure while preserving its flexibility. In addition the internal structure of genetic operators have changed in a way to support a creation of algorithms via a graphical user interface.
The goal for the future is to provide a complete development environment for evolutionary algorithms, including visual algorithm composer, Java editor, debugger and visual result analysis. This would be quite a lot of work but fortunately there is no need to invent the wheel for the 733rd time. Evolvica is based on the Eclipse Platform and uses the foundations provided by this great toolkit. All the Java features (source code editor, debugger and so on) originate from the Eclipse project.
Please note that the current state of the project is still classified as experimental. The version that you can download does not have production quality, it is a functional prototype that is still in some kind of an alpha-state. Several things are working, several others may not as expected. But you are free to try and find out for yourself and you are invited to share your experiences with others on the Evolvica user forum.
Download (31.3MB)
Added: 2005-04-01 License: Artistic License Price:
1673 downloads
bcr steps 0.2
bcr steps is a step sequencer and arpeggiator made for the Behringer BCR2000 controller. more>>
bcr steps is a step sequencer and arpeggiator made for the Behringer BCR2000 controller. The project can also be used on its own, or with almost any other MIDI controller.
If you like, you can try out the applet version, though it wont give you the full MIDI capabilities of the standalone application.
Instructions:
Download and run with java -jar bcr-steps.jar
<<lessIf you like, you can try out the applet version, though it wont give you the full MIDI capabilities of the standalone application.
Instructions:
Download and run with java -jar bcr-steps.jar
Download (0.10MB)
Added: 2007-07-01 License: GPL (GNU General Public License) Price:
849 downloads
File::Next::OO 0.04
File::Next::OO is a Perl module with file-finding iterator Wrapper for File::Next::files function. more>>
File::Next::OO is a Perl module with file-finding iterator Wrapper for File::Next::files function.
SYNOPSIS
File::Next::OO is just a wrapper around File::Next::files function. But it is easy to remember and less typing.
Call it always with object notation. Not mixed as in File::Next itself.
use File::Next::OO;
my $iter = File::Next::OO->new( /tmp, /var );
while ( my $file = $iter->() ) {
..
}
use File::Next::OO;
my $files = File::Next::OO->new(
{ file_filter => sub { -f $File::Next::OO::name and /.mp3$/ } },
"/tmp"
);
while ( my $file = $files->() ) {
print $file, $/;
}
# in array context return a list of all matches
my @files = File::Next::OO->new( "/tmp" );
# new and files are aliased use files if you like
my @files = File::Next::OO->files( "/tmp" );
# and the same with dirs
my @dirs = File::Next::OO->dirs(/tmp);
# or in peaces
my $dirs = File::Next::OO->dirs( /tmp );
while( my $dir = $dirs->() ){
print $dir, "n";
}
<<lessSYNOPSIS
File::Next::OO is just a wrapper around File::Next::files function. But it is easy to remember and less typing.
Call it always with object notation. Not mixed as in File::Next itself.
use File::Next::OO;
my $iter = File::Next::OO->new( /tmp, /var );
while ( my $file = $iter->() ) {
..
}
use File::Next::OO;
my $files = File::Next::OO->new(
{ file_filter => sub { -f $File::Next::OO::name and /.mp3$/ } },
"/tmp"
);
while ( my $file = $files->() ) {
print $file, $/;
}
# in array context return a list of all matches
my @files = File::Next::OO->new( "/tmp" );
# new and files are aliased use files if you like
my @files = File::Next::OO->files( "/tmp" );
# and the same with dirs
my @dirs = File::Next::OO->dirs(/tmp);
# or in peaces
my $dirs = File::Next::OO->dirs( /tmp );
while( my $dir = $dirs->() ){
print $dir, "n";
}
Download (0.008MB)
Added: 2007-01-13 License: Perl Artistic License Price:
1017 downloads
Neurofitter 0.9.2
A software for parameter tuning of electrophysiological neuron model. more>> Neurofitter is software for parameter tuning of electrophysiological neuron models.
It automatically searches for sets of parameters of neuron models that best fit available experimental data, and therefore acts as an interface between neuron simulators, like Neuron or Genesis, and optimization algorithms, like Particle Swarm Optimization, Evolutionary Strategies, etc.
.<<less
Download (225KB)
Added: 2009-04-07 License: Others Price: FREE
199 downloads
Ventrilo Server 2.3.1
Ventrilo is the next evolutionary step of Voice over IP (VoIP) group communications software. more>>
Ventrilo is the next evolutionary step of Voice over IP (VoIP) group communications software. Ventrilo is also the industry standard by which all others measure them selves as they attempt to imitate its features.
By offering surround sound positioning and special sound effects on a per user, per channel, per server or global configuration level the program provides each user the option to fully customize exactly how they wish to hear sounds from other users or events.
Ventrilo is best known for its superior sound quality and minimal use of CPU resources so as not to interfere with day to day operations of the computer or during online game competitions. It is also preferred for the simple user interface that any first time computer user can very quickly learn because the most commonly used features are immediately visible and can be activated with a single click of the mouse.
<<lessBy offering surround sound positioning and special sound effects on a per user, per channel, per server or global configuration level the program provides each user the option to fully customize exactly how they wish to hear sounds from other users or events.
Ventrilo is best known for its superior sound quality and minimal use of CPU resources so as not to interfere with day to day operations of the computer or during online game competitions. It is also preferred for the simple user interface that any first time computer user can very quickly learn because the most commonly used features are immediately visible and can be activated with a single click of the mouse.
Download (0.16MB)
Added: 2005-11-17 License: Freeware Price:
2164 downloads
Evocosm 3.2.0
Evocosm is a C++ Framework for Evolutionary Computing library. more>>
Evocosm is a C++ Framework for Evolutionary Computing library.
Evocosm is a set of classes that abstract the fundamental components of an evolutionary algorithm. Ill list the components here with a bit of introduction; you can review the details of the classes by downloading the code archives or by reviewing the online documentation (see the menu at the articles beginning for code and documentation links.)
All class documentation was generated from source code comments using doxygen. These docs have not been thoroughly proofread, so they may contain a few typos and minor errors. Self-publishing has taught me the value of a good proofreader.
Evolutionary algorithms come in a variety of shapes and flavors, but at their core, they all share certain characteristics: populations that reproduce and mutate through a series of generations, producing future generations based on some measure of fitness. An amazing variety of algorithms can be built on that general framework, which leads me to construct a set of core classes as the basis for future applications.
The classes include:
Random Numbers
Evocosm relies on the code in The Twisted Road to to Randomness for random number generation. The Mersenne Twister algorithm is particularly well-suited to evolutionary algorithms, based on its long period, granularity, "randomness", and speed. As of the 2.1.0 release of libevocosm, the mtprng class resides in libcoyote, and not libevocosm as it did earlier. This means that any program using libevocosm must also link against libcoyote.
Validation
To validate function arguments, Evocosm uses the code I describe in Beyond Assert; this code is part of libcoyote. Any program using libevocosm must also link against libcoyote.
Floating- Point Chromosomes
Evcosom supports the crossover and mutation of IEEE-754 floating-point numbers, using an algorithm I invented in the mid-1990s. This topic is covered in detail here.
Roulette Wheels
The roulette_wheel class implements the concept of a "software roulette wheel" for Evocosm. This is a tool for natural selection, wherein the fitness of an organism determines the width of its "slot" on an imaginary roulette wheel.
Organisms
Think of an "organism" as an answer to a problem posed by a fitness landscape; "genes" define its behavior and an associated fitness value is assigned by an evocosm during testing. Evocosm provides the freedom to define organisms as almost anything: bit strings, floating-point numbers, finite state machines, LISP programs, or external robots controlled via radio waves. In A Complexity of Options, I used an Evocosm-derived GA to determine the gcc options that produce the faster code.
Fitness Landscapes
A "fitness landscape" defines the environment where organisms "live" or a problem that they are tested against. The landscape is intimately tied to the nature of the organism; think of an organism as a potential solution to a problem implemented by the landscape. A floating-point organism, for example, could be tested by a fitness landscape that represents a function to be maximized. Or, an organism describing the shape of wing could be tested by a landscape that simulates a wind tunnel.
Evocosms
The evocosm class binds a population of organisms to a set of objects that define the rules of survival and reproduction. An evocosm will have one or more populations, which will evolve against population-unique and shared (common) fitness landscapes; breeding is controlled by a set of class objects from the following classes.
Fitness Scaling
As a population converges on an "answer", the difference between fitness values often becomes very small; this prevents the best solutions from having a significant advantage in reproduction. Fitness scaling solves this problem by adjusting the fitness values to the advantage of the most-fit chromosomes. Evocosm includes a variety of fitness scaling algorithms.
Migration
A migrator removes individuals (via "emigration") from a population of organisms, transferring them to another population (via "immigration"). The only concrete implementation of this interface is random_pool_migrator, which defines a specific number of organisms that may migrate from each population to another. When creating a random_pool_migrator, specify the number of organisms that can migrate from each population. Migration is, of course, meaningless in any application that has only one population.
Selecting Survivors
A selector decides which organisms survive from one generation to the next. Some evolutionary algorithms will not use a selector; other will. In general, it is effective to keep the "best" organisms from one generation to the next, so that good genes do not become lost at random. This is, of course, an improvement on nature, where being "the best" doesnt guarantee survival.
Reproduction
In most cases, a reproducer generates new organisms using parents selected (by fitness) from an existing population. In some singular (and probably rare) cases, a reproducer might generate new, random organisms in order to keep diversity high. Reproduction techniques can include crossover and asexual, sexual and (my favorite) try-sexual models.
Mutation Operators
A mutator applies mutations (random, usually small changes) to a set of organisms. Mutation is highly dependent on the type of organism. In traditional genetic algorithms, a mutation flips one or more bits in an integer (i.e., chromosome). Evolving a path for the Traveling Salesman Problem involves complex mutations that maintain valid permutations of destination points; in the case of floating-point numbers, Ive provided utilities for mutating and crossing IEC-60559 (IEEE- 754) float and double types.
Enhancements:
- Minor bugfixes, code cleanups, and changes to pseudo-random number generators.
- Now includes Visual Studio 2005 projects along with GNU build files.
<<lessEvocosm is a set of classes that abstract the fundamental components of an evolutionary algorithm. Ill list the components here with a bit of introduction; you can review the details of the classes by downloading the code archives or by reviewing the online documentation (see the menu at the articles beginning for code and documentation links.)
All class documentation was generated from source code comments using doxygen. These docs have not been thoroughly proofread, so they may contain a few typos and minor errors. Self-publishing has taught me the value of a good proofreader.
Evolutionary algorithms come in a variety of shapes and flavors, but at their core, they all share certain characteristics: populations that reproduce and mutate through a series of generations, producing future generations based on some measure of fitness. An amazing variety of algorithms can be built on that general framework, which leads me to construct a set of core classes as the basis for future applications.
The classes include:
Random Numbers
Evocosm relies on the code in The Twisted Road to to Randomness for random number generation. The Mersenne Twister algorithm is particularly well-suited to evolutionary algorithms, based on its long period, granularity, "randomness", and speed. As of the 2.1.0 release of libevocosm, the mtprng class resides in libcoyote, and not libevocosm as it did earlier. This means that any program using libevocosm must also link against libcoyote.
Validation
To validate function arguments, Evocosm uses the code I describe in Beyond Assert; this code is part of libcoyote. Any program using libevocosm must also link against libcoyote.
Floating- Point Chromosomes
Evcosom supports the crossover and mutation of IEEE-754 floating-point numbers, using an algorithm I invented in the mid-1990s. This topic is covered in detail here.
Roulette Wheels
The roulette_wheel class implements the concept of a "software roulette wheel" for Evocosm. This is a tool for natural selection, wherein the fitness of an organism determines the width of its "slot" on an imaginary roulette wheel.
Organisms
Think of an "organism" as an answer to a problem posed by a fitness landscape; "genes" define its behavior and an associated fitness value is assigned by an evocosm during testing. Evocosm provides the freedom to define organisms as almost anything: bit strings, floating-point numbers, finite state machines, LISP programs, or external robots controlled via radio waves. In A Complexity of Options, I used an Evocosm-derived GA to determine the gcc options that produce the faster code.
Fitness Landscapes
A "fitness landscape" defines the environment where organisms "live" or a problem that they are tested against. The landscape is intimately tied to the nature of the organism; think of an organism as a potential solution to a problem implemented by the landscape. A floating-point organism, for example, could be tested by a fitness landscape that represents a function to be maximized. Or, an organism describing the shape of wing could be tested by a landscape that simulates a wind tunnel.
Evocosms
The evocosm class binds a population of organisms to a set of objects that define the rules of survival and reproduction. An evocosm will have one or more populations, which will evolve against population-unique and shared (common) fitness landscapes; breeding is controlled by a set of class objects from the following classes.
Fitness Scaling
As a population converges on an "answer", the difference between fitness values often becomes very small; this prevents the best solutions from having a significant advantage in reproduction. Fitness scaling solves this problem by adjusting the fitness values to the advantage of the most-fit chromosomes. Evocosm includes a variety of fitness scaling algorithms.
Migration
A migrator removes individuals (via "emigration") from a population of organisms, transferring them to another population (via "immigration"). The only concrete implementation of this interface is random_pool_migrator, which defines a specific number of organisms that may migrate from each population to another. When creating a random_pool_migrator, specify the number of organisms that can migrate from each population. Migration is, of course, meaningless in any application that has only one population.
Selecting Survivors
A selector decides which organisms survive from one generation to the next. Some evolutionary algorithms will not use a selector; other will. In general, it is effective to keep the "best" organisms from one generation to the next, so that good genes do not become lost at random. This is, of course, an improvement on nature, where being "the best" doesnt guarantee survival.
Reproduction
In most cases, a reproducer generates new organisms using parents selected (by fitness) from an existing population. In some singular (and probably rare) cases, a reproducer might generate new, random organisms in order to keep diversity high. Reproduction techniques can include crossover and asexual, sexual and (my favorite) try-sexual models.
Mutation Operators
A mutator applies mutations (random, usually small changes) to a set of organisms. Mutation is highly dependent on the type of organism. In traditional genetic algorithms, a mutation flips one or more bits in an integer (i.e., chromosome). Evolving a path for the Traveling Salesman Problem involves complex mutations that maintain valid permutations of destination points; in the case of floating-point numbers, Ive provided utilities for mutating and crossing IEC-60559 (IEEE- 754) float and double types.
Enhancements:
- Minor bugfixes, code cleanups, and changes to pseudo-random number generators.
- Now includes Visual Studio 2005 projects along with GNU build files.
Download (0.34MB)
Added: 2006-09-13 License: GPL (GNU General Public License) Price:
1138 downloads
G System 0.5.1
The G System is a free and open source simulation framework and virtual reality. more>>
The G System, often simply called "G", is an effort to create exactly this: simulation of evolution. This is both, a scientific exercise and a virtual reality where many "users" can participate in an ever changing, realistic and ... evolving universe.
G is basically a framework for virtual world simulations. And not only a framework (the "G System") but a virtual reality itself, which will become the application created with the G System.
- Multi-User 3D virtual world experience. The G System provides a 3D virtual reality that can be experienced by many users at the same time. The Users are part of the evolving universe.
- Simulation. The goal of G is to simulate a realistic, dynamic and evolving universe. To allow great flexibility a well designed core system provides the infrastructure for the world. The whole world is made up with elements.
Elements allow for creation of rule systems (like realistic physics, impact of feelings,...) to be applied to elements as well as integration of artificial intelligence (and anything else that is needed) to create evolutionary abilities.
Rule systems are created with "agents". These agents can reach arbitrary complexity and are executed as a thread, thus allowing maximum freedom to the agent design. Users can enter the world and interact with their environment, which ultimately has an impact on the evolution of the virtual universe. Thus, users make up a part of the overall simulation.
Enhancements:
LIBRARIES
G Core System (GCS):
- GCS::GObject does not store the IDs of the children anymore
- GCS::GWorldData can now return a list of children of an element
G World Engine (GWE):
- Bugs fixed concerning connecting to the G Universe
- Mutex deadlocks fixed that prevented the demo to run
- XMPP Presence subscriptions are now handled and in use
- Added new GStorage class wich implements a Qt based database layer, every database supported by Qt is supported by the G System, tested and known to work so far: QSQLITE, QMYSQL3, QPSQL7
- GStorage now used instead of GXmlStorage
- All GXmlNetwork related classes have been removed
- Fixed iterator bug that could cause crashes
- The factory has been adapted to the new database options
BINARIES
guniverseclient:
- Extracted the communication/chat area into a separate widget
- Now differentiates between internal and external contacts in the
- contact list.
- The default configuration file is now in $HOME/.guniverseclient.xml
- This makes it easy to have different configurations for every user
MISC
installer:
- 2 questions less
<<lessG is basically a framework for virtual world simulations. And not only a framework (the "G System") but a virtual reality itself, which will become the application created with the G System.
- Multi-User 3D virtual world experience. The G System provides a 3D virtual reality that can be experienced by many users at the same time. The Users are part of the evolving universe.
- Simulation. The goal of G is to simulate a realistic, dynamic and evolving universe. To allow great flexibility a well designed core system provides the infrastructure for the world. The whole world is made up with elements.
Elements allow for creation of rule systems (like realistic physics, impact of feelings,...) to be applied to elements as well as integration of artificial intelligence (and anything else that is needed) to create evolutionary abilities.
Rule systems are created with "agents". These agents can reach arbitrary complexity and are executed as a thread, thus allowing maximum freedom to the agent design. Users can enter the world and interact with their environment, which ultimately has an impact on the evolution of the virtual universe. Thus, users make up a part of the overall simulation.
Enhancements:
LIBRARIES
G Core System (GCS):
- GCS::GObject does not store the IDs of the children anymore
- GCS::GWorldData can now return a list of children of an element
G World Engine (GWE):
- Bugs fixed concerning connecting to the G Universe
- Mutex deadlocks fixed that prevented the demo to run
- XMPP Presence subscriptions are now handled and in use
- Added new GStorage class wich implements a Qt based database layer, every database supported by Qt is supported by the G System, tested and known to work so far: QSQLITE, QMYSQL3, QPSQL7
- GStorage now used instead of GXmlStorage
- All GXmlNetwork related classes have been removed
- Fixed iterator bug that could cause crashes
- The factory has been adapted to the new database options
BINARIES
guniverseclient:
- Extracted the communication/chat area into a separate widget
- Now differentiates between internal and external contacts in the
- contact list.
- The default configuration file is now in $HOME/.guniverseclient.xml
- This makes it easy to have different configurations for every user
MISC
installer:
- 2 questions less
Download (2.8MB)
Added: 2005-06-12 License: GPL (GNU General Public License) Price:
1594 downloads
Bio::NEXUS 0.66
Bio::NEXUS is a Perl module providing an object-oriented, Perl-based applications programming interface (API). more>>
Bio::NEXUS is a Perl module providing an object-oriented, Perl-based applications programming interface (API).
NEXUS is a powerful and extensible format designed for use in evolutionary analysis, including the analysis of molecular sequence data as well as classical morphological and life-history data. It is the input or output format for software such as PAUP*, MacClade, Mesquite, SIMMAP, MrBayes, Nexplorer, and so on. This package also contains the demonstration applications nexplot.pl (plot character data with a tree) and nextool.pl (allowing programmatic editing, e.g., selecting particular clades or subsets of data).
The NEXPL library of Perl software (copyright 2004 Liang, Qiu and Yang) is distributed under an open source license (see LICENSE). If you use this software, please acknowledge:
Bio::NEXUS. An Object-oriented NEXUS API in Perl. T.J. Hladish, C.L. Liang, W.-G. Qiu, P. Yang, V. Gopalan and A. Stoltzfus. Available at http://www.molevol.org/camel/software.
INSTALLATION AND TESTING
This software has been tested under Linux as well as MaxOS X 10.2 and higher, and even a few Windows installations. The package installs using the standard procedure, and runs several hundred tests:
perl Makefile.PL
make
make test
make install
For more information, or to customize, see doc/Installation.pod.
<<lessNEXUS is a powerful and extensible format designed for use in evolutionary analysis, including the analysis of molecular sequence data as well as classical morphological and life-history data. It is the input or output format for software such as PAUP*, MacClade, Mesquite, SIMMAP, MrBayes, Nexplorer, and so on. This package also contains the demonstration applications nexplot.pl (plot character data with a tree) and nextool.pl (allowing programmatic editing, e.g., selecting particular clades or subsets of data).
The NEXPL library of Perl software (copyright 2004 Liang, Qiu and Yang) is distributed under an open source license (see LICENSE). If you use this software, please acknowledge:
Bio::NEXUS. An Object-oriented NEXUS API in Perl. T.J. Hladish, C.L. Liang, W.-G. Qiu, P. Yang, V. Gopalan and A. Stoltzfus. Available at http://www.molevol.org/camel/software.
INSTALLATION AND TESTING
This software has been tested under Linux as well as MaxOS X 10.2 and higher, and even a few Windows installations. The package installs using the standard procedure, and runs several hundred tests:
perl Makefile.PL
make
make test
make install
For more information, or to customize, see doc/Installation.pod.
Download (0.15MB)
Added: 2006-09-06 License: Perl Artistic License Price:
1143 downloads
Minas 1.0
Minas provides a Java application which simulates bridge construction. more>>
Minas provides a Java application which simulates bridge construction.
Minas is a bridge simulation program. The name is actually Sindarin for "tower". Thats because the codebase originally came from a program that evolutionary creates towers and builds them higher and higher. After the towers were high enough, i took the code and made this game from it.
The gameplay, usage and rules is explained there. Please make use of the feature to send snapshots to the server.
minas.jar:
This file contains the source code and the compiled class files of minas. I apologize for the lack of documentation, but (as often) this project was only an experiment at the beginning, so i never started to document it.
You need a Java compiler to build it, and the Java Runtime Environment to run it.
<<lessMinas is a bridge simulation program. The name is actually Sindarin for "tower". Thats because the codebase originally came from a program that evolutionary creates towers and builds them higher and higher. After the towers were high enough, i took the code and made this game from it.
The gameplay, usage and rules is explained there. Please make use of the feature to send snapshots to the server.
minas.jar:
This file contains the source code and the compiled class files of minas. I apologize for the lack of documentation, but (as often) this project was only an experiment at the beginning, so i never started to document it.
You need a Java compiler to build it, and the Java Runtime Environment to run it.
Download (0.076MB)
Added: 2007-03-12 License: GPL (GNU General Public License) Price:
956 downloads
EASEA 0.7b
EASEA project is a language dedicated to the specification of evolutionary algorithms. more>>
EASEA project is a language dedicated to the specification of evolutionary algorithms.
EAsea Specification of Evolutionary Algorithms (EASEA), is a high-level language dedicated to the specification of evolutionary algorithms.
The language and compiler are quite mature. EASEA compiles .ez specification files into C++ or Java object files, using existing evolutionary libraries. Supported C++ libraries currently are GALib or EO.
Enhancements:
- EASEA now features a Graphic User Interface for the Description of Experiment (GUIDE) which is in its 0.1 version (meaning it is still quite young and probably not bug-free).
- As far as EASEA is concerned, this 0.7b version does not yet handle GALib.
- Many parameters were added to pilot the quite complete EO library, and this version can be used to create JEO java experiments for the DREAM.
<<lessEAsea Specification of Evolutionary Algorithms (EASEA), is a high-level language dedicated to the specification of evolutionary algorithms.
The language and compiler are quite mature. EASEA compiles .ez specification files into C++ or Java object files, using existing evolutionary libraries. Supported C++ libraries currently are GALib or EO.
Enhancements:
- EASEA now features a Graphic User Interface for the Description of Experiment (GUIDE) which is in its 0.1 version (meaning it is still quite young and probably not bug-free).
- As far as EASEA is concerned, this 0.7b version does not yet handle GALib.
- Many parameters were added to pilot the quite complete EO library, and this version can be used to create JEO java experiments for the DREAM.
Download (0.31MB)
Added: 2006-11-01 License: GPL (GNU General Public License) Price:
1088 downloads
Optimization Algorithm Toolkit 1.3
Optimization Algorithm Toolkit is a workbench and toolkit for developing, evaluating, and playing with optimization algorithms. more>>
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.
<<lessThe 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.
Download (6.5MB)
Added: 2007-07-14 License: GPL (GNU General Public License) Price:
843 downloads
Coursor 1.0
Coursor project is a Web application for academic environments. more>>
Coursor project is a Web application for academic environments.
Coursor is a Web-based academic course system for assigning students to university courses.
It has an evolutionary algorithm for assigning students to the courses they requested. This algorithm tries to find an optimal distribution of students to existing courses while taking their preferences into account.
The system supports multiple roles for users: Admin, Student, Teacher/Prof, or Tutor.
A user can also have multiple roles.
It supports international localization, currently in English, German, French, Russian, and Arabic
Main features:
- Browser independent usage for students, teachers and admins alike (no frames, no necessary JavaScript, no cookies).
- Platform independent (entirely written in JAVA).
- Runs on APACHE Tomcat and uses a Postgresql Database.
- Evolutionary Algorithm to assign students to courses.
- Multiple Roles: Administrator, Teacher/Prof, Tutor and Student. One person can have multiple roles.
Enhancements:
- Encoding for Russian fixed
- PropertyEditor (propEdit.sf.net) programmed, will be released as own package. Eases Tranlation a lot.
- I18N target in build.xml starts PropertyEditor
- Serializable classes with ID (Eclipse wanted that)
<<lessCoursor is a Web-based academic course system for assigning students to university courses.
It has an evolutionary algorithm for assigning students to the courses they requested. This algorithm tries to find an optimal distribution of students to existing courses while taking their preferences into account.
The system supports multiple roles for users: Admin, Student, Teacher/Prof, or Tutor.
A user can also have multiple roles.
It supports international localization, currently in English, German, French, Russian, and Arabic
Main features:
- Browser independent usage for students, teachers and admins alike (no frames, no necessary JavaScript, no cookies).
- Platform independent (entirely written in JAVA).
- Runs on APACHE Tomcat and uses a Postgresql Database.
- Evolutionary Algorithm to assign students to courses.
- Multiple Roles: Administrator, Teacher/Prof, Tutor and Student. One person can have multiple roles.
Enhancements:
- Encoding for Russian fixed
- PropertyEditor (propEdit.sf.net) programmed, will be released as own package. Eases Tranlation a lot.
- I18N target in build.xml starts PropertyEditor
- Serializable classes with ID (Eclipse wanted that)
Download (1.5MB)
Added: 2006-10-19 License: GPL (GNU General Public License) Price:
1105 downloads
Step-by-Step 0.96
Step-by-Step is a simple logic game where you have to clear all colored tiles by stepping over them. more>>
Step-by-Step is a simple logic game where you have to clear all colored tiles by stepping over them.
Depending on the colour of the tile, this takes one to three steps. Step-by-Step project contains 99 levels and a separate level editor.
<<lessDepending on the colour of the tile, this takes one to three steps. Step-by-Step project contains 99 levels and a separate level editor.
Download (1.1MB)
Added: 2007-07-27 License: GPL (GNU General Public License) Price:
822 downloads
OneBone Puppy 2.00r1
Puppy Linux is an evolutionary operating system, based on GNU Linux. more>>
This is a play-thing, requested by a few people on the forum, and definitely not for the average user. In other words, Linux commandline nerds only! OneBone does not have any X GUI applications.
It does have Elinks web browser, Ytree file manager and MP text editor. The Lucent and SmartLink modem drivers are included. There are lots of text-mode applications out there and a very interesting flavour of Puppy could be created, based on this starting point.
Puppy Linux is an evolutionary operating system, based on GNU Linux. Whats different here is that Puppy is extraordinarily small, yet quite full featured. Puppy Linux can boot into a 64MB ramdisk, and thats it, the whole caboodle runs in RAM.
Unlike live CD distributions that have to keep pulling stuff off the CD, Puppy in its entirety loads into RAM. This means that all applications start in the blink of an eye and respond to user input instantly.
Puppy Linux has the ability to boot off a flash card or any USB memory device (flash-Puppy), CDROM (live-Puppy), Zip disk or LS/120/240 Superdisk (zippy-Puppy), floppy disks (floppy-Puppy), internal hard drive (hard-Puppy).
<<lessIt does have Elinks web browser, Ytree file manager and MP text editor. The Lucent and SmartLink modem drivers are included. There are lots of text-mode applications out there and a very interesting flavour of Puppy could be created, based on this starting point.
Puppy Linux is an evolutionary operating system, based on GNU Linux. Whats different here is that Puppy is extraordinarily small, yet quite full featured. Puppy Linux can boot into a 64MB ramdisk, and thats it, the whole caboodle runs in RAM.
Unlike live CD distributions that have to keep pulling stuff off the CD, Puppy in its entirety loads into RAM. This means that all applications start in the blink of an eye and respond to user input instantly.
Puppy Linux has the ability to boot off a flash card or any USB memory device (flash-Puppy), CDROM (live-Puppy), Zip disk or LS/120/240 Superdisk (zippy-Puppy), floppy disks (floppy-Puppy), internal hard drive (hard-Puppy).
Download (26.4MB)
Added: 2006-06-12 License: GPL (GNU General Public License) Price:
1234 downloads
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