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4. Evolutionary Computing

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.

4.1 EC class/code libraries

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.


A collection of programs using evolved artificial neural networks to solve a series of problems. The long term goal of the project is to advance our level of understanding about simulated evolution as a means to configure and optimize Artificial Neural Nets (ANNs). The medium term goal is to apply our methods to a series of interesting problems such as sail boat piloting and playing the game NIM.

A secondary goal is educational in nature. We attempt to write our software with ample explanation, not just for the user, but for the engineer/programmer/scientist who wants to understand the innermost detail. All of the source code is freely available to anyone to use without restriction.

All of the ANNEvolve software is implemented in C and Python.


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.


The Distributed Genetic Programming Framework (DGPF) is a scalable Java environment for heuristic, simulation-based search algorithms of any kind and Genetic Algorithms in special. We use the broad foundation of a search algorithms layer to provide a Genetic Programming system which is able to create Turing-complete code.

It's under the LGPL license. It allows you to use heuristic searches like GA and randomized Hill Climbing for any problem space you like to with just minimal programming effort. Also, you may distribute all these searches over a network, using the client/server, the peer-to-peer, or even a client/server+ peer-to-peer hybrid distribution scheme. You also can construct heterogeneous search algorithms where GA cooperates with Hill Climbing without changing any code.


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.

GAlib: Matthew's Genetic Algorithms Library

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:

  • (optional) A new Graphical User Interface, based on TCL/TK, for Unix users, allowing easy running of GALOPPS 3.2 (single or multiple subpopulations) on one or more processors. GUI writes/reads "standard" GALOPPS input and master files, and displays graphical output (during or after run) of user-selected variables.
  • 5 selection methods: roulette wheel, stochastic remainder sampling, tournament selection, stochastic universal sampling, linear-ranking-then-SUS.
  • Random or superuniform initialization of "ordinary" (non-permutation) binary or non-binary chromosomes; random initialization of permutation-based chromosomes; or user-supplied initialization of arbitrary types of chromosomes.
  • Binary or non-binary alphabetic fields on value-based chromosomes, including different user-definable field sizes.
  • 3 crossovers for value-based representations: 1-pt, 2-pt, and uniform, all of which operate at field boundaries if a non-binary alphabet is used.
  • 4 crossovers for order-based reps: PMX, order-based, uniform order-based, and cycle.
  • 4 mutations: fast bitwise, multiple-field, swap and random sublist scramble.
  • Fitness scaling: linear scaling, Boltzmann scaling, sigma truncation, window scaling, ranking.
  • Plus a whole lot more....


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.


The Genetic Algorithm Utility Library (GAUL) is a flexible programming library designed to aid development of applications that require the use of genetic algorithms. Features include:

  • Darwinian, Lamarckian or Baldwinian evolutionary schemes.
  • Both steady-state and generation-based GAs included.
  • The island model of evolution is available.
  • Chromosome datatype agnostic. A selection of common chromosome types are built-in.
  • Allows user-defined crossover, mutation, selection, adaptation and replacement operators.
  • Support for multiple, simultaneously evolved,populations.
  • Choice of high-level or low-level interface functions.
  • Additional, non-GA, optimisation algorithms are built-in for local optimisation or comparative purposes.
  • Trivial to extend using external code via the built-in code hooks.
  • May be driven by, or extended by, powerful S-Lang scripts.
  • Support for multiprocessor calculations.
  • Written using highly portable C code.


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.


This is a package for genetic algorythms and AI in Python.

Genetic can typically solve ANY problem that consists to minimize a function.

It also includes several demos / examples, like the TSP (traveling saleman problem).


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:

  • Indexed memory operation (cf. teller)
  • multi tree programs
  • Adfs
  • parameter changes without recompilation
  • populations partitioned into demes
  • (A version of) pareto fitness
This ftp site also contains a small C++ program ( to calculate the number of different there are of a given length and given function and terminal set.

gpjpp Genetic Programming in Java

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:

  • graphic output of expression trees
  • efficient diversity checking
  • Koza's greedy over-selection option for large populations
  • extensible GPRun class that encapsulates most details of a genetic programming test
  • more robust and efficient streaming code, with automatic checkpoint and restart built into the GPRun class
  • an explicit complexity limit that can be set on each GP
  • additional configuration variables to allow more testing without recompilation
  • support for automatically defined functions (ADFs)
  • tournament and fitness proportionate selection
  • demetic grouping
  • optional steady state population
  • subtree crossover
  • swap and shrink mutation


Simple genetic algorithm package written in Java.


JGAP (pronounced "jay-gap") is a Genetic Algorithms and Genetic Programming component provided as a Java framework. It provides basic genetic mechanisms that can be easily used to apply evolutionary principles to problem solutions.

JGAP was designed to be very easy to use "out of the box", while also designed to be highly modular so that more adventurous users can easily plug-in custom genetic operators and other sub-components.

patched lil-gp *

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:

  • Generic 'C' program that runs on UNIX workstations
  • Support for multiple population experiments, using arbitrary and user settable topologies for exchange, for a single processor (i.e., you can do multiple population gp experiments on your PC).
  • lil-gp manipulates trees of function pointers which are allocated in single, large memory blocks for speed and to avoid swapping.
* The patched lil-gp kernel is strongly-typed, with modifications on multithreading, coevolution, and other tweaks and features.


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.


Open BEAGLE is a C++ evolutionary computation framework. It provides a high-level software environment to do any kind of evolutionary computation, with support for tree-based genetic programming, bit string and real-valued genetic algorithms, evolution strategy, co-evolution, and evolutionary multi-objective optimization.


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:

  • Callable from Fortran or C.
  • Runs on uniprocessors, parallel computers, and workstation networks.
  • Binary-, integer-, real-, and character-valued native data types.
  • Full extensibility to support custom operators and new data types.
  • Easy-to-use interface for novice and application users.
  • Multiple levels of access for expert users.
  • Parameterized population replacement.
  • Multiple crossover, mutation, and selection operators.
  • Easy integration of hill-climbing heuristics.
  • Extensive debugging facilities.
  • Large set of example problems.
  • Detailed users guide.


Probabilistic Incremental Program Evolution (PIPE) is a novel technique for automatic program synthesis. The software is written in C. It ...

  • is easy to install (comes with an automatic installation tool).
  • is easy to use: setting up PIPE_V1.0 for different problems requires a minimal amount of programming. User-written, application-independent program parts can easily be reused.
  • is efficient: PIPE_V1.0 has been tuned to speed up performance.
  • is portable: comes with source code (optimized for SunOS 5.5.1).
  • is extensively documented(!) and contains three example applications.
  • supports statistical evaluations: it facilitates running multiple experiments and collecting results in output files.
  • includes testing tool for testing generalization of evolved programs.
  • supports floating point and integer arithmetic.
  • has extensive output features.
  • For lil-gp users: Problems set up for lil-gp 1.0 can be easily ported to PIPE_v1.0. The testing tool can also be used to process programs evolved by lil-gp 1.0.


pygene is a simple and easily understandable library for genetic algorithms and genetic programming in python. Includes examples such as the travelling salesman problem.


Your basic genetic algorithm package for python.


Small genetic programming library in C++ and ActionScript 3 (Javascript engine embedded in Flash) with flash demos.

This GP library uses the standard Koza expression tree program representation. It uses the 'grow' algorithm to generate random expressions. Mutation is performed by selecting a random subexpression in an expression tree, and replacing it with a new random expression (which satisfies the maximum tree depth constraint). Crossover (mating) between two expressions is performed by selecting a random subexpression in each parent, then exchanging them (although it only makes on child, not two).

In addition to the core code for creating, mutating, mating and evaluating expressions, the library includes a steady-state genetic algorithm with tournament selection, and a worst-out, elitist replacement policy (i.e. when a new child is created, it replaces the worse member of the population, only if it is better).

4.2 EC software kits/applications

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.

esep & xesep

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.


Grany-3 is a full-featured cellular automaton simulator, made in C++ with Gtk--, flex++/bison++, doxygen and gettext, useful to granular media physicists.


JCASim is a general-purpose system for simulating cellular automata in Java. It includes a stand-alone application and an applet for web presentations. The cellular automata can be specified in Java, in CDL, or using an interactive dialogue. The system supports many different lattice geometries (1-D, 2-D square, hexagonal, triangular, 3-D), neighborhoods, boundary conditions, and can display the cells using colors, text, or icons.


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.

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