Evolutionary computing is actually a broad term for a vast array of programming techniques, including genetic algorithms, complex adaptive systems, evolutionary programming, etc. The main thrust of all these techniques is the idea of evolution. The idea that a program can be written that will evolve toward a certain goal. This goal can be anything from solving some engineering problem to winning a game.
These are libraries of code or classes for use in programming within the evolutionary computation field. They are not meant as stand alone applications, but rather as tools for building your own applications.
daga is an experimental release of a 2-level genetic algorithm compatible with the GALOPPS GA software. It is a meta-GA which dynamically evolves a population of GAs to solve a problem presented to the lower-level GAs. When multiple GAs (with different operators, parameter settings, etc.) are simultaneously applied to the same problem, the ones showing better performance have a higher probability of surviving and "breeding" to the next macro-generation (i.e., spawning new "daughter"-GAs with characteristics inherited from the parental GA or GAs. In this way, we try to encourage good problem-solving strategies to spread to the whole population of GAs.
Ease - Evolutionary Algorithms Scripting Evironment - is an extension to the Tcl scripting language, providing commands to create, modify, and evaluate populations of individuals represented by real number vectors and/or bit strings.
EO is a templates-based, ANSI-C++ compliant evolutionary computation library. It contains classes for any kind of evolutionary computation (specially genetic algorithms) you might come up to. It is component-based, so that if you don't find the class you need in it, it is very easy to subclass existing abstract or concrete class.
This program is a FORTRAN version of a genetic algorithm driver. This code initializes a random sample of individuals with different parameters to be optimized using the genetic algorithm approach, i.e. evolution via survival of the fittest. The selection scheme used is tournament selection with a shuffling technique for choosing random pairs for mating. The routine includes binary coding for the individuals, jump mutation, creep mutation, and the option for single-point or uniform crossover. Niching (sharing) and an option for the number of children per pair of parents has been added. More recently, an option for the use of a micro-GA has been added.
Genetic Algorithm application generator and class library written mainly in C++. As a class library, and among other thing, GAGS includes:
PERL), you only need to supply it with an ANSI-C or C++ fitness function, and it creates a C++ program that uses the above library to 90% capacity, compiles it, and runs it, saving results and presenting fitness thru
GAlib contains a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and genetic operators. The documentation includes an extensive overview of how to implement a genetic algorithm as well as examples illustrating customizations to the GAlib classes.
GALOPPS is a flexible, generic GA, in 'C'. It was based upon Goldberg's Simple Genetic Algorithm (SGA) architecture, in order to make it easier for users to learn to use and extend.
GALOPPS extends the SGA capabilities several fold:
GAS means "Genetic Algorithms Stuff".
GAS is freeware.
Purpose of GAS is to explore and exploit artificial evolutions. Primary implementation language of GAS is Python. The GAS software package is meant to be a Python framework for applying genetic algorithms. It contains an example application where it is tried to breed Python program strings. This special problem falls into the category of Genetic Programming (GP), and/or Automatic Programming. Nevertheless, GAS tries to be useful for other applications of Genetic Algorithms as well.
GECO (Genetic Evolution through Combination of Objects), an extendible object-oriented tool-box for constructing genetic algorithms (in Lisp). It provides a set of extensible classes and methods designed for generality. Some simple examples are also provided to illustrate the intended use.
GPdata-3.0.tar.gz (C++) contains a version of Andy Singleton's GP-Quick version 2.1 which has been extensively altered to support:
gpjpp is a Java package I wrote for doing research in genetic programming. It is a port of the gpc++ kernel written by Adam Fraser and Thomas Weinbrenner. Included in the package are four of Koza's standard examples: the artificial ant, the hopping lawnmower, symbolic regression, and the boolean multiplexer. Here is a partial list of its features:
The GP kernel is a C++ class library that can be used to apply genetic programming techniques to all kinds of problems. The library defines a class hierarchy. An integral component is the ability to produce automatically defined functions as found in Koza's "Genetic Programming II". Technical documentation (postscript format) is included. There is also a short introduction into genetic programming.
Functionality includes; Automatically defined functions (ADFs), tournament and fitness proportionate selection, demetic grouping, optional steady state genetic programming kernel, subtree crossover, swap and shrink mutation, a way of changing every parameter of the system without recompilation, capacity for multiple populations, loading and saving of populations and genetic programs, standard random number generator, internal parameter checks.
Simple genetic algorithm package written in Java.
lil-gp is a generic 'C' genetic programming tool. It was written with a number of goals in mind: speed, ease of use and support for a number of options including:
Lithos is a stack based evolutionary computation system. Unlike most EC systems, its representation language is computationally complete, while also being faster and more compact than the S-expressions used in genetic programming. The version presented here applies the system to the game of Go, but can be changed to other problems by simply plugging in a different evaluation function. ANSI C source code is provided.
Parallel Genetic Algorithm Library
PGAPack is a general-purpose, data-structure-neutral, parallel genetic algorithm library. It is intended to provide most capabilities desired in a genetic algorithm library, in an integrated, seamless, and portable manner. Key features are in PGAPack V1.0 include:
Probabilistic Incremental Program Evolution (PIPE) is a novel technique for automatic program synthesis. The software is written in C. It
Sugal [soo-gall] is the SUnderland Genetic ALgorithm system. The aim of Sugal is to support research and implementation in Genetic Algorithms on a common software platform. As such, Sugal supports a large number of variants of Genetic Algorithms, and has extensive features to support customization and extension.
These are various applications, software kits, etc. meant for research in the field of evolutionary computing. Their ease of use will vary, as they were designed to meet some particular research interest more than as an easy to use commercial package.
ADATE (Automatic Design of Algorithms Through Evolution) is a system for automatic programming i.e., inductive inference of algorithms, which may be the best way to develop artificial and general intelligence.
The ADATE system can automatically generate non-trivial and novel algorithms. Algorithms are generated through large scale combinatorial search that employs sophisticated program transformations and heuristics. The ADATE system is particularly good at synthesizing symbolic, functional programs and has several unique qualities.
This is a new scheduler, called Evolution Scheduler, based on Genetic Algorithms and Evolutionary Programming. It lives with original Linux priority scheduler.This means you don't have to reboot to change the scheduling policy. You may simply use the manager program esep to switch between them at any time, and esep itself is an all-in-one for scheduling status, commands, and administration. We didn't intend to remove the original priority scheduler; instead, at least, esep provides you with another choice to use a more intelligent scheduler, which carries out natural competition in an easy and effective way.
Xesep is a graphical user interface to the esep (Evolution Scheduling and Evolving Processes). It's intended to show users how to start, play, and feel the Evolution Scheduling and Evolving Processes, including sub-programs to display system status, evolving process status, queue status, and evolution scheduling status periodically in as small as one mini-second.
Corewars is a game which simulates a virtual machine with a number of programs. Each program tries to crash the others. The program that lasts the longest time wins. A number of sample programs are provided and new programs can be written by the player. Screenshots are available at the Corewars homepage.
This is a virtual machine written in Java (so it is a virtual machine for another virtual machine !) for a Corewar game.
A Java (jdk-v1.0.2+) code library that is used to evolve finite state machines. The problem included in the package is the Artificial Ant problem. You should be able to compile the .java files and then run: java ArtificialAnt.
GPsys (pronounced gipsys) is a Java (requires Java 1.1 or later) based Genetic Programming system developed by Adil Qureshi. The software includes documentation, source and executables.
Grany-3 is a full-featured cellular automaton simulator, made in C++ with Gtk--, flex++/bison++, doxygen and gettext, useful to granular media physicists.
Genetic Programming (JGProg) is an open-source Java implementation of a strongly-typed Genetic Programming experimentation platform. Two example "worlds" are provided, in which a population evolves and solves the problem.