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Source code for pikaia

########################################################################
##  Copyright (C) 2006 by Marek Wojciechowski
##  <mwojc@p.lodz.pl>
##
##  Distributed under the terms of the GNU General Public License (GPL)
##  http://www.gnu.org/copyleft/gpl.html
########################################################################
"""
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Genetic algorithm based optimizer
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"""

# import raw module
from fortran import _pikaia

# Wrap main pikaia routine
[docs]def pikaia (ff, n, ff_extra_args = (), \ individuals = 100, \ generations = 500, \ digits = 6, \ crossover = 0.85, \ mutation = 2, \ initrate = 0.005, \ minrate = 0.0005, \ maxrate = 0.25, \ fitnessdiff = 1.0, \ reproduction = 3, \ elitism = 0, \ verbosity = 0): """ Pikaia version 1.2 - genetic algorithm based optimizer. Simplest usage:: from pikaia import pikaia x = pikaia(ff, n) :Parameters: ff : callable Scalar function of the signature ff(x, [n, args]), where *x* is a real array of length *n* and *args* are extra parameters. Pikaia optimizer assumes *x* elements are bounded to the interval (0, 1), thus *ff* have to aware of this, ie. probably you need some internal scaling inside *ff*. By convention, ff should return higher values for more optimal parameter values (i.e., individuals which are more "fit"). For example, in fitting a function through data points, *ff* could return the inverse of chi**2. n : int Length of *x*. Note that you do not need starting point. ff_extra_args: tuple Extra arguments passed to *ff*. individuals : int Number of individuals in a population (default is 100) generations : int Number of generations over which solution is to evolve (default is 500) digits : int Number of significant digits (i.e., number of genes) retained in chromosomal encoding (default is 6). (Note: This number is limited by the machine floating point precision. Most 32-bit floating point representations have only 6 full digits of precision. To achieve greater precision this routine could be converted to double precision, but note that this would also require a double precision random number generator, which likely would not have more than 9 digits of precision if it used 4-byte integers internally.) crossover : float Crossover probability; must be <= 1.0 (default is 0.85). If crossover takes place, either one or two splicing points are used, with equal probabilities. mutation : {1, 2, 3, 4, 5, 6} ===== ===================================================== digit description ===== ===================================================== 1 one-point mutation, fixed rate 2 one-point, adjustable rate based on fitness (default) 3 one-point, adjustable rate based on distance 4 one-point+creep, fixed rate 5 one-point+creep, adjustable rate based on fitness 6 one-point+creep, adjustable rate based on distance ===== ===================================================== initrate : float Initial mutation rate. Should be small (default is 0.005) (Note: the mutation rate is the probability that any one gene locus will mutate in any one generation.) minrate : float Minimum mutation rate. Must be >= 0.0 (default is 0.0005) maxrate : float Maximum mutation rate. Must be <= 1.0 (default is 0.25) fitnessdiff : float Relative fitness differential. Range from 0 (none) to 1 (maximum) (default is 1). reproduction : {1, 2, 3} Reproduction plan; 1/2/3 = Full generationalreplacement/Steady- state-replace-random/Steady-state-replace-worst (default is 3) elitism : {0, 1} Elitism flag; 0/1=off/on (default is 0) (Applies only to reproduction plans 1 and 2) verbosity : {0, 1, 2} Printed output 0/1/2=None/Minimal/Verbose (default is 0) :Returns: x : array (float32) The 'fittest' (optimal) solution found, i.e., the solution which maximizes fitness function *ff*. :Examples: >>> from pikaia import pikaia >>> def ff(x): return -sum(x**2) >>> pikaia(ff, 4, individuals=50, generations=200) array([ 1.23000005e-04, 7.69999970e-05, 2.99999992e-05, 2.80000004e-05], dtype=float32) .. note:: Original fortran code of pikaia is written by: Paul Charbonneau & Barry Knapp (paulchar@hao.ucar.edu, knapp@hao.ucar.edu) Wrapped with f2py by Marek Wojciechowski (mwojc@p.lodz.pl) """ # Initialize pikaia random number generator from random import randint _pikaia.rninit(randint(1, 999999999)) del randint # Restore control array ctrl = [ individuals, generations, digits, crossover, mutation, initrate, \ minrate, maxrate, fitnessdiff, reproduction, elitism, verbosity ] # Optimize x, f, status = _pikaia.pikaia(ff, n, ctrl, ff_extra_args) return x
def test(): x = pikaia(_pikaia.twod, 2) print "Solution for twod:" print x