Skip to main content

Python wrapper for pgapack, the parallel genetic algorithm library

Project description

Author:

Ralf Schlatterbeck <rsc@runtux.com>

News 08-2022: Epsilon-constrained optimization and a crossover variant that preserves permutations (so with integer genes the gene can represent a permutation).

News 03-2022: Attempt to make this installable on Windows. This involves some workaround in the code because the visual compiler does not support certain C99 constructs.

News 01-2022: This version wraps multiple evaluation with NSGA-III (note the additional ‘I’).

News 12-2021: This version wraps multiple evaluation values from your objective function: Now you can return more than one value to either use it for constraints (that must be fulfilled before the objective is optimized) or for multi-objective optimization with the Nondominated Sorting Genetic Algorithm V.2 (NSGA-II). You can combine both, multi-objective optimization and constraints.

News: This version wraps the Differential Evolution method (that’s quite an old method but is newly implemented in pgapack).

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.

To get started you need the PGAPack library, although it now comes bundled with PGApy, to install a parallel version you currently need a pre-installed PGApack compiled for the MPI library of choice. See Installation section for details.

There currently is not much documentation for PGAPy. You really, absolutely need to read the documentation that comes with PGAPack. The PGAPack user guide is now shipped together with PGAPy. It is installed together with some examples in share/pgapy, wherever the Python installer decides to place this in the final installation (usually /usr/local/share on Linux).

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 and, more recently, the differential evolution strategy.)

Note: When using NSGA_III replacement for multi (or many-) objective optimization you need to either

  • set reference points on the hyperplane intersecting all axes at offset 1. These reference points can be obtained with the convenience function pga.das_dennis, it creates a regular set of reference points using an algorithm originally publised by I. Das and J. E. Dennis. These points are then passed as the paramter reference_points to the PGA constructor.

    See examples/dtlz2.py for a usage example and the user guide for the bibliographic reference. The function gets the dimensionality of the objective space (num_eval minus num_constraint) and the number of partition to use.

  • Or set reference directions (in the objective space) with the reference_directions parameter, number of partitions for these directions with the refdir_partitions parameter (see das_dennis above, this uses Das/Dennis points internally), and a scale factor with the parameter refdir_scale.

You can set both, these parameters are not mutually exclusive.

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 sequence of numbers indicating the fitness of the gene given. It gets 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. The number of evaluations returned by your function is defined with the constructor parameter num_eval, the default for this parameter is 1. If your evaluation function does not return multiple evaluations (with the default setting of num_eval) you can either return a one-element sequence or a single return value.

  • When using multiple evaluations, these can either be used for constraints (the default) or for multi-objective optimization. In the latter case, the number of constraints (which by default is one less than the number of evaluations set with the parameter num_eval) must be set to a number that leaves at least two evaluations for objectives. The number of constraints can be set with the parameter num_constraint. When using multi-objective optimization, you need one of the two replacement-types PGA_POPREPL_NSGA_II or PGA_POPREPL_NSGA_III, set this with the pop_replace_type parameter.

  • 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.

Naming conventions in PGAPy

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. An exception of the lowercase-rule is whenever a name contains “GA” (for “genetic algorithm”), So PGASetMaxGAIterValue becomes max_GA_iter.

  • 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.

  • Whenever a name in PGApack has a “Value” or “Flag” suffix, I’ve left this out, so PGAGetFitnessCmaxValue becomes fitness_cmax and PGAGetMutationAndCrossoverFlag becomes mutation_and_crossover, the only exception to this rule is for the two functions PGAGetMutationRealValue and PGAGetMutationIntegerValue which become mutation_value not just mutation.

  • Some fields can take multiple values (they are implemented by ORing integer constants, in python they are specified as a list or tuple of constants). These are converted to plural (if not already plural in PGApack), e.g., PGASetStoppingRuleType becomes stopping_rule_types.

  • 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.

Constructor Parameters

PGApack has a lot of PGASet and PGAGet functions for setting parameters. These are reflected in constructor parameters on the one hand and in (typically read-only, but see below) properties of a PGA object on the other hand. The following table gives an overview of all the original PGApack names and the names of the python wrapper. For the PGApack name I’ve only listed the PGASet function, in many cases there is a corresponding PGAGet function. If a corresponding read-only property exists for a constructor parameter this is indicated in the “Prop” column. In some cases properties are missing because no corresponding PGAGet function is implemented in PGApack, in other cases returning a numeric value that has a symbolic constant in PGApy doesn’t make much sense.

The properties have the same name as the constructor parameter. There are Properties that don’t have a corresponding constructor parameter, namely the eval_count property (returning the count of function evaluations) and the GA_iter property that returns the current GA generation. In the type column I’m listing the Python type. If the type is followed by a number, more than one item of that type is specified (a sequence in Python). Some entries contain “sym”, these are integer values with a symbolic constant, the value “msym” indicates that several values denoted by a list of symbolic constants can be given. A special case are the PGASetRealInitRange, PGASetRealInitPercent, PGASetIntegerInitRange functions. These take two values for each allele of the gene. In python this is a sequence of 2-tuples. Note that this means that you can have different ranges of allowed values for each allele.

The num_eval property is special: Due to limitations of the C programming language, for multiple evaluations in C the first evaluation is returned as the function return-value of the evaluate function and all other parameters are returned in an auxiliary array. PGApack specifies the number of auxiliary evaluations to be returned. In Python the evaluation function can always return a sequence of evaluation values and the num_eval is one more than PGAGetNumAuxEval would return. The default for num_eval is 1.

The first two (mandatory) constructor parameters are the type of the gene (this takes a Python type, e.g., bool for a binary genome or int for an integer genome) and the length. Note that the string_length is implicitly set with the length parameter. The string_length is also available as the length of the PGA object using the Python built-in len function.

Some properties can now also be set during the run of the optimizer. These currently are crossover_prob, epsilon_exponent, multi_obj_precision, p_tournament_prob, and uniform_crossover_prob. Just assign to the member variable of the optimizer (child of PGA.pga) object.

PGApack name

Constructor parameter

Type

Prop

PGASetCrossoverBoundedFlag

crossover_bounded

int

yes

PGASetCrossoverBounceBackFlag

crossover_bounce_back

int

yes

PGASetCrossoverSBXEta

crossover_SBX_eta

float

yes

PGASetCrossoverSBXOncePerString

crossover_SBX_once_per_string

int

yes

PGASetCrossoverProb

crossover_prob

float

yes

PGASetCrossoverType

crossover_type

sym

no

PGASetDEAuxFactor

DE_aux_factor

double

yes

PGASetDECrossoverProb

DE_crossover_prob

double

yes

PGASetDECrossoverType

DE_crossover_type

sym

no

PGASetDEDither

DE_dither

double

yes

PGASetDEDitherPerIndividual

DE_dither_per_individual

bool

no

PGASetDEJitter

DE_jitter

double

yes

PGASetDENumDiffs

DE_num_diffs

int

yes

PGASetDEProbabilityEO

DE_probability_EO

double

yes

PGASetDEScaleFactor

DE_scale_factor

double

yes

PGASetDEVariant

DE_variant

sym

yes

PGASetEpsilonExponent

epsilon_exponent

float

yes

PGASetEpsilonGeneration

epsilon_generation

int

yes

PGASetEpsilonTheta

epsilon_theta

int

yes

PGAGetEvalCount

eval_count

int

yes

PGASetFitnessCmaxValue

fitness_cmax

float

yes

PGASetFitnessMinType

fitness_min_type

sym

no

PGASetFitnessType

fitness_type

sym

no

PGAIntegerSetFixedEdges

fixed_edges

no

PGAIntegerSetFixedEdges

fixed_edges_symmetric

bool

no

PGAGetGAIterValue

GA_iter

int

yes

PGASetIntegerInitPermute

integer_init_permute

int2

no

PGASetIntegerInitRange

init

no

PGASetMaxFitnessRank

max_fitness_rank

float

yes

PGASetMaxGAIterValue

max_GA_iter

int

yes

PGASetMaxNoChangeValue

max_no_change

int

no

PGASetMaxSimilarityValue

max_similarity

int

no

PGASetMixingType

mixing_type

sym

no

PGASetMultiObjPrecision

multi_obj_precision

int

yes

PGASetMutationAndCrossoverFlag

mutation_and_crossover

int

yes

PGASetMutationBounceBackFlag

mutation_bounce_back

int

yes

PGASetMutationBoundedFlag

mutation_bounded

int

yes

PGASetMutationIntegerValue

mutation_value

int

yes

PGASetMutationOrCrossoverFlag

mutation_or_crossover

int

yes

PGASetMutationPolyEta

mutation_poly_eta

float

yes

PGASetMutationPolyValue

mutation_poly_value

float

yes

PGASetMutationProb

mutation_prob

float

yes

PGASetMutationRealValue

mutation_value

float

yes

PGASetMutationType

mutation_type

sym

no

PGASetNoDuplicatesFlag

no_duplicates

int

no

PGASetNumAuxEval

num_eval

int

yes

PGASetNumConstraint

num_constraint

int

yes

PGASetNumReplaceValue

num_replace

int

yes

PGASetPopSize

pop_size

int

yes

PGASetPopReplaceType

pop_replace_type

sym

no

PGASetPrintFrequencyValue

print_frequency

int

yes

PGASetPrintOptions

print_options

msym

no

PGASetPTournamentProb

p_tournament_prob

float

yes

PGASetRandomizeSelect

randomize_select

int

yes

PGASetRandomSeed

random_seed

int

yes

PGASetRealInitRange

init

no

PGASetRealInitPercent

init_percent

no

PGASetReferenceDirections

refdir_partitions

int

no

PGASetReferenceDirections

refdir_scale

double

no

PGASetReferenceDirections

reference_directions

no

PGASetReferencePoints

reference_points

no

PGASetRestartFlag

restart

int

yes

PGASetRestartFrequencyValue

restart_frequency

int

yes

PGASetRTRWindowSize

rtr_window_size

int

yes

PGASetSelectType

select_type

sym

no

PGASetStoppingRuleType

stopping_rule_types

msym

no

PGASetStringLength

string_length

int

yes

PGASetSumConstraintsFlag

sum_constraints

int

yes

PGASetTournamentSize

tournament_size

int

yes

PGASetTournamentWithReplacement

tournament_with_replacement

int

yes

PGASetTruncationProportion

truncation_proportion

float

yes

PGASetUniformCrossoverProb

uniform_crossover_prob

float

yes

Note: The mutation_or_crossover and mutation_and_crossover parameters are deprecated, use mixing_type instead!

PGA Object Methods

The following are the methods that can be used during the run of the genetic search. The run method is used to start the search. This can be used, to, e.g., set an allele during hill-climbing in a custom endofgen method. Note that some methods only apply to certain gene types, e.g. the encode_int_ methods can only be used on binary alleles (they encode an integer value as a binary or gray code representation into the gene). Other methods take or return different types depending on the type of gene, e.g. get_allele or set_allele, they call different backend functions depending on the gene type. With the set_random_seed method, the random number generator can be re-seeded. It is usually best to seed the generator once at (before) the beginning by specifying random_seed in the constructor. For further details consult the user guide. The method get_evaluation will return a double for a single evaluation and a tuple of double for multiple evaluations (when num_eval is >1)

Method

Parameters

Return

check_stopping_conditions

True if stop should occur

encode_int_as_binary

p, pop, frm, to, val

None

encode_int_as_gray_code

p, pop, frm, to, val

None

encode_real_as_binary

p, pop, frm, to l, u, val

None

encode_real_as_gray_code

p, pop, frm, to l, u, val

None

euclidian_distance

p1, pop1 p2, pop2

float

fitness

pop

None

get_allele

p, pop, index

allele value

get_best_index

pop

index of best string

get_best_report_index

pop, idx

index of best eval with idx

get_evaluation

p, pop

evaluation of p

get_evaluation_up_to_date

p, pop

True if up-to-date

get_fitness

p, pop

fitness of p (float)

get_int_from_binary

p, pop, frm, to

int

get_int_from_gray_code

p, pop, frm, to

int

get_iteration

deprecated, use GA_iter

get_real_from_binary

p, pop, frm, to, l, u

float

get_real_from_gray_code

p, pop, frm, to, l, u

float

random01

float between 0 and 1

random_flip

probability

0 or 1

random_gaussian

mean, stddev

float

random_interval

l, r

int between l, r

random_uniform

l, r

float between l, r

run

None

select_next_index

pop

index selected individual

set_allele

p, pop, i, value

None

set_evaluation

p, pop, value

None

set_evaluation_up_to_date

p, pop, status

None

set_random_seed

seed

None (use constructor!)

User-Methods

PGApack has the concept of user functions. These allow customization of different areas of a genetic algorihm. In Python they are implemented as methods that can be changed in a derived class. One of the methods that must be implemented in a derived class is the evaluate function (although technically it is not a user function in PGApack). It interprets the gene and returns an evaluation value or a sequence of evaluation values if you set the num_eval constructor parameter. PGApack computes a fitness from the raw evaluation value. For some methods an up-call into the PGA class is possible, for some methods this is not possible (and in most cases not reasonable). Note that for the stop_cond method, the standard check for stopping conditions can be called with:

self.check_stopping_conditions()

The following table lists the overridable methods with their parameters (for the function signature the first parameter self is omitted). Note that in PGApack there are additional user functions that are needed for user-defined data types which are currently not exposed in Python. In the function signatures p denotes the index of the individual and pop denotes the population. If more than one individual is specified (e.g., for crossover) these can be followed by a number. For crossover c1 and c2 denote the destination individuals (children). The propability for the mutation method is a floating-point value between 0 and 1. Remember to count the number of mutations that happen, and return that value for the mutation method!

Method

Call Signature

Return Value

Up-Call

check_duplicate

p1, pop1, p2, pop2

True if dupe

no

stop_cond

True to stop

no

crossover

p1, p2, p_pop, c1, c2, c_pop

None

no

endofgen

None

no

evaluate

p, pop

sequence of float

no

gene_difference

p1, pop1, p2, pop2

float

no

initstring

p, pop

None

no

mutation

p, pop, propability

#mutations

no

pre_eval

pop

None

no

print_string

file, p, pop

None

yes

Constants

The following PGApack constants are available:

Constant

Description

PGA_CROSSOVER_EDGE

Edge crossover for permutations

PGA_CROSSOVER_ONEPT

One-point Crossover

PGA_CROSSOVER_SBX

Simulated Binary Crossover

PGA_CROSSOVER_TWOPT

Two-point Crossover

PGA_CROSSOVER_UNIFORM

Uniform Crossover

PGA_FITNESSMIN_CMAX

Map fitness by subtracting worst

PGA_FITNESSMIN_RECIPROCAL

Map fitness via reciprocal

PGA_FITNESS_NORMAL

Linear normalization of fitness

PGA_FITNESS_RANKING

Linear fitness ranking

PGA_FITNESS_RAW

Identity fitness function

PGA_MUTATION_CONSTANT

Mutation by adding/subtracting constant

PGA_MUTATION_GAUSSIAN

Mutation by selecting from Gaussian distribution

PGA_MUTATION_PERMUTE

Mutation swaps two random genes

PGA_MUTATION_POLY

Polynomial Mutation

PGA_MUTATION_RANGE

Replace gene with uniform selection from init range

PGA_MUTATION_UNIFORM

Mutation uniform from interval

PGA_NEWPOP

Symbolic constant for new population

PGA_OLDPOP

Symbolic constant for old population

PGA_POPREPL_BEST

Population replacement best strings

PGA_POPREPL_NSGA_II

Use NSGA-II replacement for multi-objective opt.

PGA_POPREPL_NSGA_III

Use NSGA-III replacement for multi-objective opt.

PGA_POPREPL_PAIRWISE_BEST

Compare same index in old and new population

PGA_POPREPL_RANDOM_NOREP

Population replacement random no replacement

PGA_POPREPL_RANDOM_REP

Population replacement random with replacement

PGA_POPREPL_RTR

Restricted Tournament Replacement

PGA_REPORT_AVERAGE

Report average evaluation

PGA_REPORT_HAMMING

Report hamming distance

PGA_REPORT_OFFLINE

Report offline

PGA_REPORT_ONLINE

Report online

PGA_REPORT_STRING

Report the string

PGA_REPORT_WORST

Report the worst evaluation

PGA_SELECT_LINEAR

Return individuals in population order

PGA_SELECT_PROPORTIONAL

Fitness-proportional selection

PGA_SELECT_PTOURNAMENT

Binary probabilistic tournament selection

PGA_SELECT_SUS

Stochastic universal selection

PGA_SELECT_TOURNAMENT

Tournament selection

PGA_SELECT_TRUNCATION

Truncation selection

PGA_STOP_MAXITER

Stop on max iterations

PGA_STOP_NOCHANGE

Stop on max number of generations no change

PGA_STOP_TOOSIMILAR

Stop when individuals too similar

Missing Features

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.

Reporting Bugs

Please use the Sourceforge Bug Tracker or the Github Bug Tracker and

  • 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

Resources

Project information and download from Sourceforge main page

or checkout from Github

or directly install via pypi.

Installation

PGApy, as the name suggests, supports parallelizing the evaluation function of the genetic algorithm. This uses the Message Passing Interface (MPI) standard.

To install a serial version (without parallel programming using MPI) you can simply install from pypi using pip. Alternatively when you have unpacked or checked out from sources you can install with:

python3 setup.py install --prefix=/usr/local

If you want a parallel version using an MPI (Message-Passing Interface) library you will have to install a parallel version of PGApack first. The easiest way to do this is to use my pgapack debian package builder from github. Clone this repository, check out the branch debian/sid, install the build dependencies, they’re listed in the file debian/control and build the debian packages using:

dpkg-buildpackage -rfakeroot

This builds pgapack debian packages for all supported MPI libraries in debian, currently these are mpich, openmpi, and lam. In addition to the MPI libraries a serial version of the pgapack library is also built. Proceed by installing the package pgapack and the MPI backend library of choice. If you don’t have a preference for an MPI library, libpgapack-openmpi is the package that uses the Debians default preferences of an MPI library.

Once a parallel version of PGApack is installed, you can install PGApy as follows: You set environment variables for the PGA_PARALLEL_VARIANT (one of mpich, openmpi, or lam) and set the PGA_MODULE to module_from_parallel_install. Finally you envoke the setup, e.g.:

export PGA_PARALLEL_VARIANT=openmpi
export PGA_MODULE=module_from_parallel_install
python3 setup.py install --prefix=/usr/local

If your MPI library is installed in a different place you should study the Extension configurations in setup.py to come up with an Extension definition that fits your installation. If your installation is interesting to more people, feel free to submit a patch that adds your Extension-configuration to the standard setup.py.

Changes

Version 1.8: Epsilon-constrained optimization

  • Epsilon-constrained optimization

  • Precision for printing evals in multi-objective optimization, use this feature for making regression-test work on AMD where a floating-point difference in the 16th or so decimal place made a test fail

  • Crossover for permutations

  • Version-numbers: try to match pgapack, we might still diverge in the last digit, though

Version 1.2: Many-objective optimization with NSGA-III

  • Implement NSGA-III

Version 1.1.6: Polynomial mutation and simulated binary crossover (SBX)

  • Simulated binary crossover (SBX)

  • Polynomial mutation

Version 1.1.1-1.1.5: Small PGAPack updates, fixes for non-debian

  • Fix setup.py for non-debian systems

  • Update to latest PGAPack with small changes

Version 1.1: Add multi-objective optimization with NSGA-II

  • Wrap latest pgapack version 1.4

  • This add multi-objective optimization using the Nondominated Sorting Genetic Algorithm version 2 (NSGA-II) by Deb et. al. This makes use of the previously-introduced option to return more than one value in the objective function. To use the feature you need to set the num_constraint parameter to a value that leave some of the function values returned by your evaluation function as objective function values (and not as constraints). See example in examples/multi.py.

Version 1.0: Add constraint handling

  • Wrap latest pgapack version 1.3

  • This adds auxiliary evaluations. Now your evaluation function can return multiple floating-point values as a sequence if you set the num_eval paramter >1 in the constructor. Currently additional evaluation values are used for constraint handling. Constraint values are minimized. Once they reach zero or a negative value they no longer count: The sum of all positive constraints is the overall constraint violation. For details see paper by Deb, 2000, see user guide for citation. If you’re not using constraints, nothing in your code needs changes.

  • This release may change the path an optimization takes. So for the same seed of the random number generator you will get a different result, at least if during the search there are individuals with the same evaluation (and different genetic material). This is due to a change of the sort function in pgapack (it switched to a stable sort from the C standard library).

Version 0.9: Allow installation of parallel version

  • Pass argv (or sys.argv) to PGACreate

  • Add a stanza to setup.py to allow a parallel installation with a given pgapack variant compiled for an MPI library. This currently needs a pre-installed pgapack debian package.

Version 0.8: Bugfix in real mutation

  • Fix a core-dump in the latest pgapack

Version 0.7: Major changes in wrapping

  • Now Differential Evolution is implemented, see the minfloat example and the user guide of pgapack.

Version 0.6: Major changes in wrapping

  • Now the wrapping uses the standard Python recommendations on how to create a custom class.

  • Update documentation

  • Rename fitness_cmax (from fitness_cmax_value)

  • Better error checking of parameters

Version 0.5: Bug-fix release

  • Now the setup.py works, previous version had an encoding problem

  • Wrap some minor new methods

  • Bug-fix in PGAPack truncation selection

Version 0.4: Bundle PGAPack

  • The PGAPack package is now included as a git submodule. By default we build against this library

  • License fixes: The module long shipped a COPYING file that includes the 2-clause BSD license. But the headers of setup.py and pgamodule.c still included another license. This has been corrected.

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

  • Add the __module__ string to class PGA in module pga. Now calling:: help (pga) in python works as expected, previously no help-text was given for the included module

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PGAPy-1.8.tar.gz (718.3 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page