#---------------------------------------------------------------------------
#
# Evolution.py: basics of evolutionary dynamics, Evolution chapter
#
# see Quasispecies.py and Tournament.py for application examples
#
# by Lidia Yamamoto, Belgium, July 2013
#
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# Copyright (C) 2015 Lidia A. R. Yamamoto
# Contact: http://www.artificial-chemistries.org/
#
# This file is part of PyCellChemistry.
#
# PyCellChemistry is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# version 3, as published by the Free Software Foundation.
#
# PyCellChemistry is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with PyCellChemistry, see file COPYING. If not, see
# http://www.gnu.org/licenses/
#

Modules
		artchem.BinaryStrings numpy sys

Classes
		Evolution class Evolution Methods defined here: __init__(self) create a random initial population of molecules with intentionally bad fitness avgfitness(self) compute the average fitness of the population bestworstfit(self, mset) find the best and worst individuals in a given multiset fitness(self, binstr) calculate the fitness of an individual (normalized to one) optimum(self) produce an optimum individual for the desired fitness function randmol(self) generate a random molecule in the form of an N-bit integer

Generated automatically by pydoc, July 10, 2015

Generated automatically by pydoc, July 10, 2015