PGAPy 0.5

Author:

Ralf Schlatterbeck <rsc@runtux.com>

PGAPy is a wrapper for pgapack, the parallel genetic algorithm library (see pgapack Readme), a powerfull genetic algorithm library by D. Levine, Mathematics and Computer Science Division Argonne National Laboratory. The library is written in C. PGAPy wraps this library for use with Python. The original pgapack library is already quite old but is one of the most complete and accurate (and fast, although this is not my major concern when wrapping it to python) genetic algorithm implementations out there with a lot of bells and whistles for experimentation. It also has shown a remarkably small number of bugs over the years. It supports parallel execution via the message passing interface MPI in addition to a normal “serial” version. That’s why I wanted to use it in Python, too.

There currently is not much documentation for PGAPy. You really, absolutely need to read the documentation that comes with pgapack — and of course you need the pgapack library.

The original pgapack library can still be downloaded from the pgapack ftp site, it is written in ANSI C and therefore should run on most platforms. Note that this version is not very actively maintained. I’ve started a pgapack fork on github where I’ve ported the library to the latest version of the MPI standard and have fixed some minor inconsistencies in the documentation. I’ve also implemented some new features (notably enhancements in selection schemes and a new replacement strategy called restricted tournament replacement)

I have tested pgapy on Linux only and I’ll currently not provide Windows versions. You also can find my pgapack fork on github this repository has the three upstream releases as versions in git and contains some updates concerning support of newer MPI versions and documentation updates. I’ve also included patches in the git repository of the Debian maintainer of the package, Dirk Eddelbuettel.

To get you started, I’ve included some very simple examples in examples, e.g., one-max.py implements the “Maxbit” example similar to one in the pgapack documentation. The examples were inspired by the book “Genetic Algorithms in Python” but are written from scratch and don’t include any code from the book. The examples illustrates several points:

• Your class implementing the genetic algorithm needs to inherit from pga.PGA (pga is the PGAPy wrapper module).

• You need to define an evaluation function called evaluate that returns a number indicating the fitness of the gene given with the parameters p and pop that can be used to fetch allele values from the gene using the get_allele method, for more details refer to the pgapack documentation.

• You can define additional functions overriding built-in functions of the pgapack library, illustrated by the example of print_string. Note that we could call the original print_string method of our PGA superclass. In the same way you can implement, e.g., your own crossover method.

• The constructor of the class needs to define the Gene type, in the examples we use int and bool built-in datatypes.

• The length of the gene needs to be given in the constructor.

• We often want to maximize the numbers returned by our evaluation function, set the parameter maximize to False if you want to minimize.

• For non-binary genes we can define an array of init values, each entry containing a sequence with lower and upper bound. The array has to have the length of the gene. Note that the upper bound is included in the range of possible values (unlike the python range operator but compatible with the pgapack definition).

• In the constructor of the class we can add parameters of the genetic algorithm. Not all parameters of pgapack are wrapped yet, currently you would need to consult the sourcecode of PGAPy to find out which parameters are wrapped. In the example we define several print options.

• Finally the genetic algorithm is started with the run method.