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"""Evolution Strategies for a Population.
Evolver classes manage a population of individuals, and are responsible
for taking care of the transition from one generation to the next.
"""
# standard modules
import sys
class SteadyStateEvolver:
"""Evolve a population in place.
This implements a Steady State GA, where the population of individuals
is evolved in place.
"""
def __init__(self):
raise NotImplementedError("Need to code this.")
class GenerationEvolver:
"""Evolve a population from generation to generation.
This implements a Generational GA, in which the population moves from
generation to generation.
"""
def __init__(self, starting_population, selector):
"""Initialize the evolver.
Arguments:
o starting_population -- An initial set of individuals to begin
the evolution process from. This should be a list of Organism
objects.
o selector -- A Selection object that implements selection, along
with mutation and crossover to select a new population from a
given population.
"""
self._population = starting_population
self._selector = selector
def evolve(self, stopping_criteria):
"""Evolve the population through multiple generations.
Arguments:
o stoppping_criteria -- A function which, when passed the current
individuals in the population, will determine when to stop
the evolution process.
Returns:
o The final evolved population.
"""
while not(stopping_criteria(self._population)):
try:
# perform selection, mutation, crossover on the population
self._population = self._selector.select(self._population)
# update the fitness of the new popultation
for organism in self._population:
organism.recalculate_fitness()
# dump out all of the organisms for debugging if the
# evolution process is broken with a Control-C
except KeyboardInterrupt:
# sort the population so we can look at duplicates
self._population.sort()
for org in self._population:
print org
sys.exit()
return self._population
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