|Name||Modified||Size||Downloads / Week||Status|
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|Author:||Ralf Schlatterbeck <firstname.lastname@example.org>|
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 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.
For the Debian Linux distribution, pgapack is included in oldstable (jessie), you can still install this in the current stable release (stretch). This version does not yet include the fixes for newer MPI versions and documentation updates.
For debian the pre-built documentation is in /usr/share/doc/pgapack/user_guide.ps.gz
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.
- 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.
When you extend PGAPy — remember not all functions of pgapack are wrapped yet and you may need additional functions — you should stick to my naming conventions when making changes. The following naming conventions were used for the wrapper:
- Constants of pgapack like PGA_REPORT_STRING are used as-is in uppercase. These constants can be directly imported from the wrapper module. Not all constants are wrapped so far, if you need more, add them to the constdef array in pgamodule.c and send me a patch.
- For methods of the pga.PGA class I've removed the PGA prefix used throughout pgapack and converted the method to lowercase with underscores between uppercase words in the original function name, so PGARun becomes run, PGACheckStoppingConditions becomes check_stopping_conditions.
- Where possible I've made a single class method where pgapack needs a separate function for each datatype, so PGAGetBinaryAllele, PGAGetCharacterAllele, PGAGetIntegerAllele, PGAGetRealAllele all become get_allele. Same holds true for set_allele.
- Internal method names in the wrapper program have a leading PGA_ — so the class method set_allele is implemented by the C-function PGA_set_allele in pgamodule.c.
As already mentioned, not all functions and constants of pgapack are wrapped yet — still for many applications the given set should be enough. If you need additional functions, you may want to wrap these and send me a patch.
Another feature of pgapack is currently not implemented in the wrapper, the usage of custom datatypes. With pgapack you can define your own datatypes complete with their custom implementations of the genetic algorithm functionality like crossover, mutation, etc. I don't expect problems implementing these, though.
- give a short description of what you think is the correct behaviour
- give a description of the observed behaviour
- tell me exactly what you did.
- if you can publish your source code this makes it a lot easier to debug for me
Project information and download from Sourceforge main page
or checkout from Github
or directly install via pypi.
Version 0.3: Feature enhancements, Bug fixes
Port to Python3, Python2 is still supported, license change.
- C-Code of wrapper updated to support both, Python2 and Python3
- Update documentation
- Fix some memory leaks that could result when errors occurred during some callback methods
- License change: We now have the 2-clause BSD license (similar to the MPICH license of pgapack), this used to be LGPL.
Version 0.2: Feature enhancements, Bug fixes
64 bit support, more pgapack functions and attributes wrapped, Readme-update: Sourceforge logo, Changes chapter.
- Bug-fixes for 64 bit architectures
- More functions and attributes of pgapack wrapped
- Add a build-rule to setup.py to allow building for standard-install of pgapack — this currently needs editing of setup.py — should use autodetect here but this would require that I set up a machine with standard install of pgapack for testing.
- Add Sourceforge logo as required
- Add Changes chapter for automagic releases
Version 0.1: Initial freshmeat announcement
PGAPy is a wrapper for pgapack, the parallel genetic algorithm library, a powerful genetic algorithm library. PGAPy wraps this library for use with Python. Pgapack is one of the most complete and accurate genetic algorithm implementations out there with a lot of features for experimentation.
- Initial Release