Genetic Algorithms: Cool Name & Damn Simple

Nice GA tutorial.

Genetic algorithms are a mysterious sounding technique in mysterious sounding field--artificial intelligence. This is the problem with naming things appropriately. When the field was labeled artificial intelligence, it meant using mathematics to artificially create the semblance of intelligence, but self-engrandizing researchers and Isaac Asimov redefined it as robots.

The name genetic algorithms does sound complex and has a faintly magical ring to it, but it turns out that they are one of the simplest and most-intuitive concepts you'll encounter in A.I.




Genetic Algorithms: Cool Name & Damn Simple - Irrational Exuberance

Curve fitting with Pyevolve

This is a very nice tutorial for genetic algorithms.  It uses pyevolve but the tutorial part is useful even if you are using a different language/implementation for GA.

A Coder's Musings: Curve fitting with Pyevolve

pygene - simple python genetic algorithms/programming library

I played around with this a bit before I decided on pyevolve instead.  However, pygene might suit your needs better.

pygene - simple python genetic algorithms/programming library

blaa/PyGene · GitHub

Genetic Algorithms tutorial

Great tutorial and introduction to genetic algorithms.  There are java applets that you can play with to see how GA's work.

These pages introduce some fundamentals of genetic algorithms. Pages are intended to be used for learning about genetic algorithms without any previous knowledge from this area. Only some knowledge of computer programming is assumed. You can find here several interactive Java applets demonstrating work of genetic algorithms.

As the area of genetic algorithms is very wide, it is not possible to cover everything in these pages. But you should get some idea, what the genetic algorithms are and what they could be useful for. Do not expect any sophisticated mathematics theories here.

Main page - Introduction to Genetic Algorithms - Tutorial with Interactive Java Applets

Pyevolve genetic algorithm python software

I have used this software to successfully create a genetic algorithm python script that I use to tune parameters on extra tree classifiers and RDF classifiers.  It is pretty easy to use and you can make almost any type of GA with it.  It is open source so you can go in a tinker with it.

Welcome to Pyevolve documentation ! — Pyevolve v0.5 documentation

There is a great pyevolve tutorial here:

A Coder's Musings: Curve fitting with Pyevolve

Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy

A hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) is proposed for predictive models in the environmental sciences. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Three environmental forecast datasets used in the WCCI-2006 contest – surface air temperature, precipitation and sulphur dioxide concentration – were tested. We used multiple linear regression (MLR) as benchmark and a variety of machine learning techniques including bootstrap-aggregated ensemble artificial neural network (ANN), SVR-ES, SVR with hyper-parameters given by the Cherkassky–Ma estimate, the M5 regression tree, and random forest (RF). We also tested all techniques using stepwise linear regression (SLR) first to screen out irrelevant predictors. We concluded that SVR-ES is an attractive approach because it tends to outperform the other techniques and can also be implemented in an almost automatic way. The Cherkassky–Ma estimate is a useful approach for minimizing the mean absolute error and saving computational time related to the hyper-parameter search. The ANN and RF are also good options to outperform multiple linear regression (MLR). Finally, the use of SLR for predictor selection can dramatically reduce computational time and often help to enhance accuracy.

Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy

inspyred 1.0 genetic algorithm

I have not used this package but it looks well documented.


inspyred is a free, open source framework for creating biologically-inspired computational intelligence algorithms in Python, including evolutionary computation, swarm intelligence, and immunocomputing. Additionally, inspyred provides easy-to-use canonical versions of many bio-inspired algorithms for users who don't need much customization.

inspyred 1.0 : Python Package Index

Genetic Algorithm Library at Code Project

Genetic Algorithm Library is a C++ library for building genetic algorithms.  The home page for the project has an excellent tutorial that not only explains how to use the library but is also a great introduction to genetic algorithms for someone new to the field.

Genetic Algorithm Library - CodeProject