Introduction

This is the documentation of the Pyevolve release 0.5. Since the version 0.4, Pyevolve has changed too much, 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:

• Python 2.5+

• Optional, for graph plotting: Matplotlib 0.98.4+

  The matplotlib [1] is required to plot the graphs.

Footnotes

[1]	Matplotlib is Copyright (c) 2002-2008 John D. Hunter; All Rights Reserved

Downloads

Windows

Installers for Microsoft Windows platform:

Pyevolve v.0.5 (installer) for Python 2.5

This is an .exe installer for Microsoft Windows XP/Vista

Pyevolve v.0.5 (installer) for Python 2.6

This is an .exe installer for Microsoft Windows XP/Vista

Linux

Installation package for Linux platform:

Pyevolve v.0.5 (egg package) for Python 2.5

This is an egg package file

Pyevolve v.0.5 (egg package) for Python 2.6

This is an egg package file

Examples and Source code

Examples and source code for Pyevolve 0.5:

Pyevolve v.0.5 source code (package)

This is an package with the Pyevolve source code

Examples for Pyevolve v.0.5 (package)

This is an package with the Pyevolve examples

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

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__
'0.5'

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.

GA Features

Chromosomes / Representations

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

Note

it is important to note, that the 1D List and the 2D list 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

2D List

Uniform Crossover, Single Vertical Point Crossover, Single Horizontal Point Crossover

Mutator Methods

1D 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

2D List

Swap Mutator, Integer Gaussian Mutator, Real Gaussian Mutator, Allele Mutator

Initializators

1D Binary String

Binary String Initializator

1D List

Allele Initializator, Integer Initializator, Real Initializator

2D List

Allele Initializator, Integer Initializator, Real Initializator

Scaling Methods

Linear Scaling, Sigma Truncation Scaling and Power Law Scaling, Raw Scaling

Selection Methods

Rank Selection, Uniform Selection, Tournament Selection, 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

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.

See also

Wikipedia: Genetic Algorithm

An article talking about Genetic Algorithms.