Metadata-Version: 1.1
Name: PGAPy
Version: 1.1
Summary: Python wrapper for pgapack, the parallel genetic algorithm library
Home-page: http://pgapy.sourceforge.net/
Author: Ralf Schlatterbeck
Author-email: rsc@runtux.com
License: UNKNOWN
Description: PGAPy: Python Wrapper for PGAPack Parallel Genetic Algorithm Library
        ====================================================================
        
        .. |--| unicode:: U+2013   .. en dash
        
        :Author: Ralf Schlatterbeck <rsc@runtux.com>
        
        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.)
        
        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.
        
        .. _`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``.
        - 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 read-only 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 is one Property that is no constructor parameter, namely 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.
        
        ==================================== =============================== ====== ====
        PGApack name                         Constructor parameter           Type   Prop
        ==================================== =============================== ====== ====
        ``PGASetCrossoverProb``              ``crossover_prob``              float  yes
        ``PGASetCrossoverType``              ``crossover_type``              sym    no
        ``PGASetFitnessCmaxValue``           ``fitness_cmax``                float  yes
        ``PGASetFitnessType``                ``fitness_type``                sym    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
        ``PGASetMutationAndCrossoverFlag``   ``mutation_and_crossover``      int    yes
        ``PGASetMutationBoundedFlag``        ``mutation_bounded``            int    yes
        ``PGASetMutationIntegerValue``       ``mutation_value``              int    yes
        ``PGASetMutationOrCrossoverFlag``    ``mutation_or_crossover``       int    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
        ``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
        ==================================== =============================== ====== ====
        
        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.
        
        ============================= ================== ===========================
        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_evaluation``            *p, pop*           evaluation of *p* (float)
        ``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,*          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_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_ONEPT        One-point 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_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_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
        
        .. _`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 ``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 1.1: 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
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
