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PGAPy-2.0
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مشاهده بیشترتوضیحات
Python wrapper for pgapack, the parallel genetic algorithm library
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | PGAPy-2.0 |
| نام | PGAPy |
| نسخه کتابخانه | 2.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ralf Schlatterbeck |
| ایمیل نویسنده | rsc@runtux.com |
| آدرس صفحه اصلی | http://pgapy.sourceforge.net/ |
| آدرس اینترنتی | https://pypi.org/project/PGAPy/ |
| مجوز | - |
PGAPy: Python Wrapper for PGAPack Parallel Genetic Algorithm Library
====================================================================
.. |--| unicode:: U+2013 .. en dash
.. |epsilon| unicode:: U+03B5 .. epsilon
:Author: Ralf Schlatterbeck <rsc@runtux.com>
News
----
News 10-2022: Add user defined datatypes. Example in ``examples/gp`` use
user defined data types to implement genetic programming (we represent
expressions by a tree data structure). This uses the new serialization
API in pgapack to transfer a Python pickle representation to peer MPI
processes. Also incorporate the latest changes in pgapack which
optimizes duplicate checking. This is of interest for large population
sizes in the genetic programming examples. Note that the
``gene_difference`` method has been renamed to ``gene_distance``.
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).
Introduction
------------
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 but will probably not compile against
a recent version of MPI_. It will also not work with recent versions of
PGAPy. Note that this version is not 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, a new replacement strategy called
*restricted tournament replacement* [1]_, [2]_, [4]_ and, more recently,
the differential evolution strategy [5]_, [6]_. In addition this version
now supports multi objective optimization with NSGA-II [7]_ and
many-objective optimization with NSGA-III [8]_, [9]_. It also supports
the Epsilon Constraint method [10]_.
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 [12]_.
These points are then passed as the parameter ``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'm mainly testing pgapy on Linux. But I've recently made it run on
Windows, too but I'm not very actively testing on Windows. Let me know
if you run it on Windows, sucessfully or not sucessfully.
As mentioned above, you 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.
I'm actively maintaining that branch, adding new features and bug-fixes.
.. _`PGAPack Readme`:
https://github.com/schlatterbeck/pgapack/blob/master/README.rst
.. _PGAPack: http://ftp.mcs.anl.gov/pub/pgapack/
.. _`PGAPack fork on github`: https://github.com/schlatterbeck/pgapack
.. _MPI: http://mpi-forum.org/
.. _`my pgapack debian package builder`:
https://github.com/schlatterbeck/debian-pgapack
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), the
``GA_iter`` property that returns the current GA generation, and the
``mpi_rank`` property that returns the MPI rank of the current process
(this is sorted under PGAGetRank).
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
``PGAGetRank`` ``mpi_rank`` 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,* None
*frm, to, val*
``encode_int_as_gray_code`` *p, pop,* None
*frm, to, val*
``encode_real_as_binary`` *p, pop, frm, to* None
*l, u, val*
``encode_real_as_gray_code`` *p, pop, frm, to* None
*l, u, val*
``euclidian_distance`` *p1, pop1* float
*p2, pop2*
``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_gene`` *p, pop* get gene (user data types)
``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,* float
*frm, to, l, u*
``get_real_from_gray_code`` *p, pop,* float
*frm, to, l, u*
``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_gene`` *p, pop, gen* set gene (user data types)
``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_distance`` *p1, pop1, p2, pop2* float no
``hash`` *p, pop* int 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
========================== ===========================================
User Defined Data Types
-----------------------
The latest version of PGAPy features user defined data types. Just
define your data type and pass it as the second parameter to the
``PGA`` constructor. The framework will take care of serializing the
data when transmitting via ``MPI`` (if you're running a parallel
version).
If duplicate checking is enabled via the ``no_duplicates`` constructor
parameter, your data type needs to define a ``__hash__`` method (unless
the python default hash method fulfills your requirements).
User defined data types do not use alleles, so the normal ``get_allele``
(and ``set_allele``) methods are not available. Instead the full
individual can be retrieved with the ``get_gene`` method and set with
the ``set_gene`` method.
With user data types you need to define the following methods:
- ``check_duplicate (self, p1, pop1, p2, pop2)`` if you enable duplicate
checking with the crossover parameter ``no_duplicates``. This should
return True when the two individuals are duplicates. Use ``get_gene``
to retrieve the genes for the individuals ``p1`` and ``p2`` in
populations ``pop1`` and ``pop2``.
- ``crossover (self, p1, p2, ppop, c1, c2, cpop)`` for crossover
operation, use ``get_gene`` for getting the parent genes for the
parents ``p1`` and ``p2`` in generation ``ppop`` and use ``set_gene``
for setting the child genes ``c1`` and ``c2`` in generation ``cpop``.
- ``initstring (self, p, pop)`` for initializing the given string, use
``set_gene`` in that method for setting your object as a gene.
- ``mutation (self, p, pop, pm)`` for the mutation operation. This
should return the number of mutations performed. If duplicate checking
is enabled, the framework will repeatedly call the mutation operator
for mutating a duplicate individual into another individual that is no
duplicate. This uses the return value of your mutation method. You
will enter an endless loop if your mutation operator does not
occasionally return an non-zero number of mutatations performed when
duplicate checking is enabled. The ``pm`` parameter gives the mutation
probability. Use ``get_gene`` for retrieving the individual to be
mutated and use ``set_gene`` to update this individual after mutation.
- ``print_string (self, file, p, pop)`` to print a gene object, use
``get_gene`` for retrieving the individual to be printed.
For these methods it is generally a good idea to never modify an
individual in-place: This individual may be repeatedly used in genetic
operations (e.g. mutation and crossover), so when modifying it you will
produce erroneous results for later genetic operations. To copy a data
structure, python's ``deepcopy`` function in the module ``copy`` is
usually used.
In addition to the methods above you may want to define a stopping rule
with a ``stop_cond`` method or override the way a hash is computed using
a ``hash`` method. The default for computing a hash is to call
``hash (gene)`` where gene is an object of the user defined data type.
Other methods that may be used is an ``endofgen`` method, a
``gene_distance`` method (e.g., when using Restricted Tournament
Replacement, with ``PGA_POPREPL_RTR``), or a ``pre_eval`` method.
An example with user defined data types is in ``examples/gp``: This
implements Genetic Programming with a tree data structure. Note that the
``Node`` class in ``gp.py`` has a ``__hash__`` method that builds a hash
over the serialization of the tree (which is the same for individuals
with the same tree structure).
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.
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
.. _`Sourceforge Bug Tracker`:
http://sourceforge.net/tracker/?group_id=152022&atid=782852
.. _`Github Bug Tracker`:
https://github.com/schlatterbeck/pgapy/issues
.. _send: mailto:rsc@runtux.com
Resources
---------
Project information and download from `Sourceforge main page`_
.. _`Sourceforge main page`: http://sourceforge.net/projects/pgapy/
or checkout from Github_
.. _`Github`: http://github.com/schlatterbeck/pgapy
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 ``master``,
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``.
References
----------
.. [1] Georges Harik. Finding multiple solutions in problems of bounded
difficulty. IlliGAL Report 94002, Illinois Genetic Algorithm Lab,
May 1994.
.. [2] Georges R. Harik. Finding multimodal solutions using restricted
tournament selection. In Eshelman [3]_, pages 24–31.
.. [3] Larry J. Eshelman, editor. *Proceedings of the 6th International
Conference on Genetic Algorithms (ICGA)*. Morgan Kaufmann, July 1995.
.. [4] Martin Pelikan. *Hierarchical Bayesian Optimization Algorithm:
Toward a New Generation of Evolutionary Algorithms*, volume 170 of
Studies in Fuzziness and Soft Computing. Springer, 2005.
.. [5] Rainer Storn and Kenneth Price. Differential evolution |--| a simple
and efficient heuristic for global optimization over continuous
spaces. *Journal of Global Optimization*, 11(4):341–359, December
1997.
.. [6] Kenneth V. Price, Rainer M. Storn, and Jouni A. Lampinen.
*Differential Evolution: A Practical Approach to Global
Optimization.* Springer, Berlin, Heidelberg, 2005.
.. [7] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A
fast and elitist multiobjective genetic algorithm: NSGA-II. *IEEE
Transactions on Evolutionary Computation*, 6(2):182–197, April 2002.
.. [8] Kalyanmoy Deb and Himanshu Jain. An evolutionary many-objective
optimization algorithm using reference-point-based nondominated
sorting approach, part I: Solving problems with box constraints.
*IEEE Transactions on Evolutionary Computation*, 18(4):577–601,
August 2014.
.. [9] Himanshu Jain and Kalyanmoy Deb. An evolutionary many-objective
optimization algorithm using reference-point-based nondominated
sorting approach, part II: Handling constraints and extending to
an adaptive approach. *IEEE Transactions on Evolutionary
Computation*, 18(4):602–622, August 2014.
.. [10] Tetsuyuki Takahama and Setsuko Sakai. Constrained optimization
by the |epsilon| constrained differential evolution with an
archive and gradient-based mutation. In [11]_.
.. [11] *IEEE Congress on Evolutionary Computation (CEC)*. Barcelona,
Spain, July 2010.
.. [12] Indraneel Das and J. E. Dennis. Normal-boundary intersection: A new
method for generating the pareto surface in nonlinear multicriteria
optimization problems. SIAM Journal on Optimization, 8(3):631–657,
August 1998.
Changes
-------
Version 2.0: User defined data types
- Implement user defined data types, note that your data type can be
variable-size, e.g., a tree data structure. The framework takes care
of serializing the data type and transmitting it to a remote MPI
process if using a parallel version.
- When duplicate checking is enabled with the constructor parameter
``no_duplicates``, the underlying pgapack code now uses a hash table.
This means the effort is no longer quadratic in the population size
but linear.
- Example of Genetic Programming (GP) in the ``examples/gp`` directory
- Rename the gene_difference method to gene_distance
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 parameter >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
نحوه نصب
نصب پکیج whl PGAPy-2.0:
pip install PGAPy-2.0.whl
نصب پکیج tar.gz PGAPy-2.0:
pip install PGAPy-2.0.tar.gz