Navigation

Introduction

This is the documentation of the Pyevolve release 0.6rc1. Since the version 0.5, Pyevolve has changed in many aspects, many new features was added and many bugs was fixed, this documentation describes those changes, the new API and new features.

Pyevolve was developed to be a complete genetic algorithm framework written in pure python, the main objectives of Pyevolve is:

  • written in pure python, to maximize the cross-platform issue;
  • easy to use API, the API must be easy for end-user;
  • see the evolution, the user can and must see and interact with the evolution statistics, graphs and etc;
  • extensible, the API must be extensible, the user can create new representations, genetic operators like crossover, mutation and etc;
  • fast, the design must be optimized for performance;
  • common features, the framework must implement the most common features: selectors like roulette wheel, tournament, ranking, uniform. Scaling schemes like linear scaling, etc;
  • default parameters, we must have default operators, settings, etc in all options;
  • open-source, the source is for everyone, not for only one.

Requirements

Pyevolve can be executed on Windows, Linux and Mac platforms.

Note

On the Mac platform, it’s reported that Pyevolve 0.5 can’t enter on the Interactive Mode.

Pyevolve can be executed under Jython 2.5b1+, but with some restrictions:
  • You can’t use some features like the SQLite3 adapter to dump statistics and graphs (unless you install Matplotlib on Jython, but I think that still is not possible).
Pyevolve can be executed under IronPython 2.x, but with some restrictions:
  • You can’t use some features like the SQLite3 adapter to dump statistics and graphs (unless you install Matplotlib on Jython, but I think that still is not possible).
  • You must install a zlib module for IronPython.

Pyevolve requires the follow modules:

Footnotes

[1]Matplotlib is Copyright (c) 2002-2008 John D. Hunter; All Rights Reserved
[2]VPython was originated by David Scherer in 2000.
[3]Pydot was developed by Ero Carrera.
[4]MySQLdb was developed by Andy Dustman and contributors.

Downloads

Windows

Installers for Microsoft Windows platform:

Pyevolve v.0.6rc1 (installer) for Python 2.5
This is an .exe installer for Microsoft Windows XP/Vista
Pyevolve v.0.6rc1 (installer) for Python 2.6
This is an .exe installer for Microsoft Windows XP/Vista
Pyevolve v.0.6rc1 (source code) for Python 2.x
This is the source code

Linux

Installation package for Linux platform:

Pyevolve v.0.6rc1 (source code) for Python 2.x
This is the source code

Examples package

Examples package for Pyevolve v.0.6rc1:

Pyevolve examples v.0.6rc1 (examples)
This is an package with the Pyevolve source code

Installation

You can download the specific Pyevolve from the Downloads section, or using easy_install.

The installation can be easy done by using the easy_install:

easy_install pyevolve

You can upgrade your older version too:

easy_install --upgrade pyevolve

or install a downloaded egg package:

easy_install /downloads/downloaded_package.egg

or install from an URL:

easy_install http://site/package.egg

This command will automatic search, download and install a suitable version of pyevolve, once you have installed, you can test:

>>> import pyevolve
>>> print pyevolve.__version__
'v.0.6rc1'

easy_install utility is part of setuptools. Once you have installed setuptools, you will find the easy_install.exe program in your Python Scripts subdirectory.

Genetic Algorithm Features

Chromosomes / Representations

1D List, 2D List, 1D Binary String, 2D Binary String and Tree

Note

it is important to note, that the 1D List, 2D List and Tree can carry any type of python objects or primitives.

Crossover Methods

1D Binary String
Single Point Crossover, Two Point Crossover, Uniform Crossover
1D List
Single Point Crossover, Two Point Crossover, Uniform Crossover, OX Crossover, Edge Recombination Crossover, Cut and Crossfill Crossover, Real SBX Crossover
2D List
Uniform Crossover, Single Vertical Point Crossover, Single Horizontal Point Crossover
2D Binary String
Uniform Crossover, Single Vertical Point Crossover, Single Horizontal Point Crossover
Tree
Single Point Crossover, Strict Single Point Crossover

Mutator Methods

1D Binary String
Swap Mutator, Flip Mutator
2D Binary String
Swap Mutator, Flip Mutator
1D List
Swap Mutator, Integer Range Mutator, Real Range Mutator, Integer Gaussian Mutator, Real Gaussian Mutator, Integer Binary Mutator, Allele Mutator, Simple Inversion Mutator
2D List
Swap Mutator, Integer Gaussian Mutator, Real Gaussian Mutator, Allele Mutator, Integer Range Mutator
Tree
Swap Mutator, Integer Range Mutator, Real Range Mutator, Integer Gaussian Mutator, Real Gaussian Mutator

Initializators

1D Binary String
Binary String Initializator
2D Binary String
Binary String Initializator
1D List
Allele Initializator, Integer Initializator, Real Initializator
2D List
Allele Initializator, Integer Initializator, Real Initializator
Tree
Integer Initializator, Allele Initializator

Scaling Methods

Linear Scaling, Sigma Truncation Scaling and Power Law Scaling, Raw Scaling, Boltzmann Scaling, Exponential Scaling, Saturated Scaling

Selection Methods

Rank Selection, Uniform Selection, Tournament Selection, Tournament Selection Alternative (doesn’t uses the Roulette Wheel), Roulette Wheel Selection

Genetic Programming Features

Chromosomes / Representations

Tree

Warning

the Tree of Genetic Programming is the class GTree.GTreeGP and not the GTree.GTree class of the Genetic Algorithm representation.

Crossover Methods

Tree
Single Point Crossover

Mutator Methods

Tree
Operation Mutator, Subtree mutator

Initializators

Tree
Grow Initializator, Full Initializator, Ramped Half-n-Half

Scaling Methods

Linear Scaling, Sigma Truncation Scaling and Power Law Scaling, Raw Scaling, Boltzmann Scaling, Exponential Scaling, Saturated Scaling

Selection Methods

Rank Selection, Uniform Selection, Tournament Selection, Tournament Selection Alternative (doesn’t uses the Roulette Wheel), Roulette Wheel Selection

Genetic Algorithms Literature

In this section, you will find study material to learn more about Genetic Algorithms.

Books

Goldberg, David E (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, Boston, MA.

Goldberg, David E (2002), The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Addison-Wesley, Reading, MA.

Fogel, David B (2006), Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, Piscataway, NJ. Third Edition

Holland, John H (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor

Michalewicz, Zbigniew (1999), Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag.

See also

Wikipedia: Genetic Algorithms
The Wikipedia article about Genetic Algorithms.

Sites

Introduction to Genetic Algorithms
A nice introduction by Marek Obitko.
A Field Guide to Genetic Programming
A book, freely downloadable under a Creative Commons license.
A Genetic Algorithm Tutorial by Darrell Whitley Computer Science Department Colorado State University
An excellent tutorial with lots of theory

Genetic Programming Literature

In this section, you will find study material to learn more about Genetic Programming.

Books

Poli, Riccardo; Langdon, William B.; McPhee, Nicholas F., A Field Guide to Genetic Programming, this book is also available online (a GREAT initiative from authors) in Book Site

Koza, John R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.

See also

Wikipedia: Genetic Programming
The Wikipedia article about Genetic Programming.

Sites

Introduction to Genetic Programming
A nice collection of GP related content !
A Field Guide to Genetic Programming
A book, freely downloadable under a Creative Commons license.
The Genetic Programming Bibliography
A very interesting initiative mantained by William Langdon, Steven Gustafson, and John Koza. Over than 6000 GP references !

Glossary / Concepts

Raw score
The raw score represents the score returned by the Evaluation function, this score is not scaled.
Fitness score
The fitness score is the scaled raw score, for example, if you use the Linear Scaling (Scaling.LinearScaling()), the fitness score will be the raw score scaled with the Linear Scaling method. The fitness score represents how good is the individual relative to our population.
Evaluation function

Also called Fitness Function or Objective Function, the evaluation function is the function which evaluates the genome, giving it a raw score. The objective of this function is to quantify the solutions (individuals, chromosomes)

See also

Wikipedia: Fitness Function
An article talking about the Evaluation function, or the “Fitness Function”.
Sample genome
The sample genome is the genome which are used as configuration base for all the new replicated genomes.
Interactive mode

Pyevolve have an interactive mode, you can enter in this mode by pressing ESC key before the end of the evolution. When you press ESC, a python environment will be load. In this environment, you have some analysis functions and you can interact with the population of individuals at the specific generation.

See also

Module Interaction
The Interaction module.
Step callback function
This function, when attached to the GA Engine (GSimpleGA.GSimpleGA), will be called every generation. It receives one parameter, the GA Engine by itself.
Data Type Independent
When a genetic operator is data type idependent, it will operates on different data types but not with different chromosome representation, for example, the Mutators.G1DListMutatorSwap() mutator will operate on Real, Allele or Integer G1DList.G1DList chromosome, but not on G2DList.G2DList chromosome.
Standardized Fitness

The standardized fitness restates the raw score so that a lower numerical value is always a better value.

See also

Genetic Programming: On the Programming of Computers by Means of Natural Selection
A book from John R. Koza about Genetic Programming.
Adjusted Fitness

The adjusted fitness is a measure computed from the Standardized Fitness, the Adjusted Fitness is always between 0 and 1 and it’s always bigger for better individuals.

See also

Genetic Programming: On the Programming of Computers by Means of Natural Selection
A book from John R. Koza about Genetic Programming.
Non-terminal node
The non-terminal node or non-terminal function is a function in a parse tree which is either a root or a branch in that tree, in the GP we call non-terminal nodes as “functions”, the opposite of terminal nodes, which are the variables of the GP.

See also

Wikipedia: Genetic Algorithm
An article talking about Genetic Algorithms.
Wikipedia: Genetic Programming
The Wikipedia article about Genetic Programming.

Other platforms and performance

Running Pyevolve on Symbian OS (PyS60)

Pyevolve is compatible with PyS60 2.0 (but older versions of the 1.9.x trunk should work fine too); PyS60 2.0 is a port of Python 2.5.4 core to the S60 smartphones, it was made by Nokia and it’s Open Source. All smartphones based on the S60 2nd and 3rd editions should run PyS60, you can download it from the Maemo garage project home.

To install Pyevolve in PyS60 you simple need to copy the “pyevolve” package (you can use the sources of Pyevolve or even the “pyevolve” of your Python installation to the smartphone in a place that PyS60 can find it, usually in c:\resource\Python25, for more information read the PyS60 documentation. The Genetic Algorithms and the Genetic Programming cores of Pyevolve was tested with PyS60 2.0, but to use Genetic Programming, you must define explicitly the funtions of the GP, like in How to manually add non-terminal functions to Genetic Programming core.

Of course not all features of Pyevolve are supported in PyS60, like for example some DBAdapters and the graphical plotting tool, since no matplotlib port is available to PyS60 at the moment. Pyevolve was tested with PyS60 2.0 in a Nokia N78 and in a Nokia N73 smartphones.

See also

Croozeus.com - home to PyS60 developers
A lot of information and tutorials about PyS60, very recommended.
Python for S60 - OpenSource
The PyS60 project wiki.

Running Pyevolve on Jython

Jython is an implementation of Python language and it’s modules (not all unfortunatelly) which is designed to run over the Java platform. Pyevolve was tested against Jython 2.5.x and worked well, except for the Genetic Programming core which is taking a lot of memory, maybe a Jython issue with the Java JVM.

You’re highly encouraged to run Jython with the JVM “-server” option; this option will enable anoter VM JIT which is optimal for applications where the fast startup times isn’t important, and the overall performance is what matters. This JIT of the “Server mode” has different policies to compile your code into native code, and it’s well designed for long running applications, where the VM can profile and optimize better than the JIT of “Client mode”.

Pyevolve was tested against Jython 2.5.1 in Java v.1.6.0_18 Java(TM) SE Runtime Environment (build 1.6.0_18-b07) Java HotSpot(TM) Client VM (build 16.0-b13, mixed mode, sharing)

See also

Jython
Official Jython project home.
Java HotSpot
The Java HotSpot Performance Engine Architecture.

Running Pyevolve on IronPython

IronPython is an open-source implementation of the Python programming language targeting the .NET Framework and Mono, written entirely in C# and created by Jim Hugunin. IronPython is currently language-compatible with Python 2.6.

Pyevolve was tested against the IronPython 2.6 (2.6.10920.0) in a Windows XP SP3 with .NET 2.0.50727.3603.

See also

Official IronPython project home
Official IronPython project home.
Differences between IronPython and CPython
Documents with differences between IronPython and CPython (the official Python interepreter).
IronPython performance benchmarks
A lot of benchmarks and comparisons between IronPython and CPython.

Running Pyevolve on iPod/iPhone

The Genetic Algorithm core of Pyevolve was tested on iPod Touch 2G with the firmware v.3.1.2. To use it, you first must install the port of Python 2.5+ to the OS of iPod. You just need to put the Pyevolve package inside the directory where you’ll call your application or just put it inside another place where the Python from iPod/iPhone can found in path.

See also

Miniguide to install Python on iPhone
Miniguide on how to install Python on iPhone

Improving Pyevolve performance

Pyevolve, at least for the versions <= 0.6, have all modules written in pure Python, which enables some very useful features and portability, but sometimes weights in performance. Here are some ways users and developers uses to increase the performance of Pyevolve:

Psyco
Psyco is the well know Python specializing compiled, created by Armin Rigo. Psyco is very easy to use and can give you a lot of speed up.
Cython
Cython is a specific language used to create C/C++ extensions for Python, it is based on the Python language itself, so if you think Psyco is not enought or aren’t giving too much optimizations, you can use Cython to create your own C/C++ extensions; the best approach is to use Cython to build your Evaluation function, which is usually the most consuming part of Genetic Algorithms.

See also

Psyco at Sourceforge.net
The official site of Psyco at Sourceforge.net
Psyco 2.0 binaries for Windows
Development of psyco was recently done by Christian Tismer. Here you’ll find the binaries of Psyco 2.0 (Python 2.4, 2.5 and 2.6) for Windows.
Cython - C-Extensions for Python
Official Cython project home.


Table Of Contents

Previous topic

What’s new ?

Next topic

Get Started - Tutorial

This Page

Navigation

© Copyright 2010, Christian S. Perone. Created using Sphinx 0.6.5.