Source code for population

"""Implements the core evolution algorithm."""
from __future__ import print_function

from neat.reporting import ReporterSet
from neat.math_util import mean
from neat.six_util import iteritems, itervalues

[docs]class CompleteExtinctionException(Exception): pass
[docs]class Population(object): """ This class implements the core evolution algorithm: 1. Evaluate fitness of all genomes. 2. Check to see if the termination criterion is satisfied; exit if it is. 3. Generate the next generation from the current population. 4. Partition the new generation into species based on genetic similarity. 5. Go to 1. """ def __init__(self, config, initial_state=None): self.reporters = ReporterSet() self.config = config stagnation = config.stagnation_type(config.stagnation_config, self.reporters) self.reproduction = config.reproduction_type(config.reproduction_config, self.reporters, stagnation) if config.fitness_criterion == 'max': self.fitness_criterion = max elif config.fitness_criterion == 'min': self.fitness_criterion = min elif config.fitness_criterion == 'mean': self.fitness_criterion = mean elif not config.no_fitness_termination: raise RuntimeError( "Unexpected fitness_criterion: {0!r}".format(config.fitness_criterion)) if initial_state is None: # Create a population from scratch, then partition into species. self.population = self.reproduction.create_new(config.genome_type, config.genome_config, config.pop_size) self.species = config.species_set_type(config.species_set_config, self.reporters) self.generation = 0 self.species.speciate(config, self.population, self.generation) else: self.population, self.species, self.generation = initial_state self.best_genome = None def add_reporter(self, reporter): self.reporters.add(reporter) def remove_reporter(self, reporter): self.reporters.remove(reporter)
[docs] def run(self, fitness_function, n=None): """ Runs NEAT's genetic algorithm for at most n generations. If n is None, run until solution is found or extinction occurs. The user-provided fitness_function must take only two arguments: 1. The population as a list of (genome id, genome) tuples. 2. The current configuration object. The return value of the fitness function is ignored, but it must assign a Python float to the `fitness` member of each genome. The fitness function is free to maintain external state, perform evaluations in parallel, etc. It is assumed that fitness_function does not modify the list of genomes, the genomes themselves (apart from updating the fitness member), or the configuration object. """ if self.config.no_fitness_termination and (n is None): raise RuntimeError("Cannot have no generational limit with no fitness termination") k = 0 while n is None or k < n: k += 1 self.reporters.start_generation(self.generation) # Evaluate all genomes using the user-provided function. fitness_function(list(iteritems(self.population)), self.config) # Gather and report statistics. best = None for g in itervalues(self.population): if best is None or > best = g self.reporters.post_evaluate(self.config, self.population, self.species, best) # Track the best genome ever seen. if self.best_genome is None or > self.best_genome = best if not self.config.no_fitness_termination: # End if the fitness threshold is reached. fv = self.fitness_criterion( for g in itervalues(self.population)) if fv >= self.config.fitness_threshold: self.reporters.found_solution(self.config, self.generation, best) break # Create the next generation from the current generation. self.population = self.reproduction.reproduce(self.config, self.species, self.config.pop_size, self.generation) # Check for complete extinction. if not self.species.species: self.reporters.complete_extinction() # If requested by the user, create a completely new population, # otherwise raise an exception. if self.config.reset_on_extinction: self.population = self.reproduction.create_new(self.config.genome_type, self.config.genome_config, self.config.pop_size) else: raise CompleteExtinctionException() # Divide the new population into species. self.species.speciate(self.config, self.population, self.generation) self.reporters.end_generation(self.config, self.population, self.species) self.generation += 1 if self.config.no_fitness_termination: self.reporters.found_solution(self.config, self.generation, self.best_genome) return self.best_genome