<gh_stars>1-10
#!/usr/bin/env python
"""
Genetic algorithm class
"""

import random
import operator
import pymongo
db_client=pymongo.MongoClient("192.168.3.11",27017)
db=db_client["binaryCSA"]
exp_col=db["experiments"]

class GeneticAlgorithm(object):
    """Evolve a population iteratively to find better
    individuals on each generation. If elitism is set, the
    fittest individual of a generation will be part of the
    next one.
    """

    def __init__(self, population, tournament_size=5, elitism=True):
        self.population = population
        self.x_train, self.y_train = self.population.get_data()
        self.tournament_size = tournament_size
        self.elitism = elitism
        self.generation = 1

    def get_population_type(self):
        return self.population.__class__

    def run(self, max_generations):
        print("Starting genetic algorithm...\n")
        while self.generation <= max_generations:
            self.evolve_population()
            self.generation += 1

    def evolve_population(self):
        if self.population.get_size() < self.tournament_size:
            raise ValueError("Population size is smaller than tournament size.")
        print("Evaluating generation #{}...".format(self.generation))
        fittest = self.population.get_fittest()
        print("Fittest individual is:")
        print(fittest)
        print("Fitness value is: {}\n".format(round(fittest.get_fitness(), 4)))
        new_population = self.get_population_type()(
            self.population.get_species(), self.x_train, self.y_train, individual_list=[],
            maximize=self.population.get_fitness_criteria()
        )
        if self.elitism:
            new_population.add_individual(self.population.get_fittest())
        while new_population.get_size() < self.population.get_size():
            child = self.tournament_select().reproduce(self.tournament_select())
            child.mutate()
            new_population.add_individual(child)
        self.population = new_population

    def tournament_select(self):
        tournament = self.get_population_type()(
            self.population.get_species(), self.x_train, self.y_train, individual_list=[
                self.population[i] for i in random.sample(range(self.population.get_size()), self.tournament_size)
            ], maximize=self.population.get_fitness_criteria()
        )

        return tournament.get_fittest()


class RussianRouletteGA(GeneticAlgorithm):
    """Simpler genetic algorithm used in the Genetic CNN paper.
    """

    def __init__(self, population, crossover_probability=0.2, mutation_probability=0.8):
        super(RussianRouletteGA, self).__init__(population)
        self.crossover_probability = crossover_probability
        self.mutation_probability = mutation_probability

    def evolve_population(self, eps=1e-15):
        print("Evaluating generation #{}...".format(self.generation))
        fittest = self.population.get_fittest()
        print("Fittest individual is:")
        print(fittest)
        print("Fitness value is: {}\n".format(round(fittest.get_fitness(), 4)))
        # Russian roulette selection
        if self.population.get_fitness_criteria():
            weights = [self.population[i].get_fitness() for i in range(self.population.get_size())]
        else:
            weights = [1 / (self.population[i].get_fitness() + eps) for i in range(self.population.get_size())]
        min_weight = min(weights)
        weights = [weight - min_weight for weight in weights]
        if sum(weights) == .0:
            weights = [1. for _ in range(self.population.get_size())]
        new_population = self.get_population_type()(
            self.population.get_species(), self.x_train, self.y_train, individual_list=[
                self.population[i].copy() for i in random.choices(
                    range(self.population.get_size()), weights=weights, k=self.population.get_size()
                )
            ], maximize=self.population.get_fitness_criteria()
        )
        # Crossover and mutation
        for i in range(new_population.get_size() // 2):
            if random.random() < self.crossover_probability:
                new_population[i].crossover(new_population[i + 1])
            else:
                if random.random() < self.mutation_probability:
                    new_population[i].mutate()
                if random.random() < self.mutation_probability:
                    new_population[i + 1].mutate()
        self.population = new_population


class CrowSearchAlgorithm(object):
    """Evolve a population iteratively to find better
    individuals on each generation. If elitism is set, the
    fittest individual of a generation will be part of the
    next one.
    """

    def __init__(self, flock, tournament_size=5):
        self.flock = flock
        self.x_train, self.y_train = self.flock.get_data()
        self.tournament_size = tournament_size
        self.iteration = 1
        self.max_iterations=0
        self.explored=[crow.get_location() for crow in self.flock]
        unique=[]
        for location in self.explored:
            if location not in unique:
                unique.append(location)
        print(self.explored)
        print(unique)
        print("Flock Size",len(self.explored),";","Unique Crows",len(unique))
    def get_flock_type(self):
        return self.flock.__class__

    def run(self, max_iterations,exp_no):
        print("\nStarting Crow Search Algorithm...")
        self.max_iterations=max_iterations
        while self.iteration <= max_iterations:
            exp_col.update_one({"no":exp_no},{"$push":{"iterations":[]}})
            self.release_flock()
            self.iteration += 1

    def release_flock(self):
        if self.flock.get_size() < self.tournament_size:
            raise ValueError("Flock size is smaller than tournament size.")
        print("\nRunning iteration #{}...".format(self.iteration))
        fittest = self.flock.get_fittest()
        print("\nBest performance is {:.8f} by Crow {} on the location :".format(fittest.get_best_fitness(),fittest.get_id()),fittest.get_memory())
        # print(fittest.get_memory())
        # print("Fitness value is: {:.8f}\n".format(fittest.get_best_fitness()))
        if self.iteration <self.max_iterations:
            for i,_ in enumerate(self.flock):
                crow=self.flock[i]
                while crow.get_location() in self.explored:
                    crow.follow(self.tournament_select(crow))
                self.explored.append(crow.get_location())
            # new_locations = [crow.get_location() for crow in self.flock]
            # repeated=[]
            # for location in new_locations:
            #     if location in self.explored:
            #         repeated.append(location)
            # print("Flock Size", len(new_locations), ";", "Repeated Crows", len(repeated))
            # for r in repeated:
            #     print(r)


    def tournament_select(self,crow):
        individual_list=[self.flock[i] for i in random.sample(range(self.flock.get_size()), self.tournament_size) if crow.get_id() !=self.flock[i].get_id()]
        target=max(individual_list, key=operator.methodcaller('get_best_fitness'))
        return target

#!/usr/bin/env python
"""
Genetic algorithm class
"""

import random
import operator
import pymongo
db_client=pymongo.MongoClient("192.168.3.11",27017)
db=db_client["binaryCSA"]
exp_col=db["experiments"]

class GeneticAlgorithm(object):
    """Evolve a population iteratively to find better
    individuals on each generation. If elitism is set, the
    fittest individual of a generation will be part of the
    next one.
    """

    def __init__(self, population, tournament_size=5, elitism=True):
        self.population = population
        self.x_train, self.y_train = self.population.get_data()
        self.tournament_size = tournament_size
        self.elitism = elitism
        self.generation = 1

    def get_population_type(self):
        return self.population.__class__

    def run(self, max_generations):
        print("Starting genetic algorithm...\n")
        while self.generation <= max_generations:
            self.evolve_population()
            self.generation += 1

    def evolve_population(self):
        if self.population.get_size() < self.tournament_size:
            raise ValueError("Population size is smaller than tournament size.")
        print("Evaluating generation #{}...".format(self.generation))
        fittest = self.population.get_fittest()
        print("Fittest individual is:")
        print(fittest)
        print("Fitness value is: {}\n".format(round(fittest.get_fitness(), 4)))
        new_population = self.get_population_type()(
            self.population.get_species(), self.x_train, self.y_train, individual_list=[],
            maximize=self.population.get_fitness_criteria()
        )
        if self.elitism:
            new_population.add_individual(self.population.get_fittest())
        while new_population.get_size() < self.population.get_size():
            child = self.tournament_select().reproduce(self.tournament_select())
            child.mutate()
            new_population.add_individual(child)
        self.population = new_population

    def tournament_select(self):
        tournament = self.get_population_type()(
            self.population.get_species(), self.x_train, self.y_train, individual_list=[
                self.population[i] for i in random.sample(range(self.population.get_size()), self.tournament_size)
            ], maximize=self.population.get_fitness_criteria()
        )

        return tournament.get_fittest()


class RussianRouletteGA(GeneticAlgorithm):
    """Simpler genetic algorithm used in the Genetic CNN paper.
    """

    def __init__(self, population, crossover_probability=0.2, mutation_probability=0.8):
        super(RussianRouletteGA, self).__init__(population)
        self.crossover_probability = crossover_probability
        self.mutation_probability = mutation_probability

    def evolve_population(self, eps=1e-15):
        print("Evaluating generation #{}...".format(self.generation))
        fittest = self.population.get_fittest()
        print("Fittest individual is:")
        print(fittest)
        print("Fitness value is: {}\n".format(round(fittest.get_fitness(), 4)))
        # Russian roulette selection
        if self.population.get_fitness_criteria():
            weights = [self.population[i].get_fitness() for i in range(self.population.get_size())]
        else:
            weights = [1 / (self.population[i].get_fitness() + eps) for i in range(self.population.get_size())]
        min_weight = min(weights)
        weights = [weight - min_weight for weight in weights]
        if sum(weights) == .0:
            weights = [1. for _ in range(self.population.get_size())]
        new_population = self.get_population_type()(
            self.population.get_species(), self.x_train, self.y_train, individual_list=[
                self.population[i].copy() for i in random.choices(
                    range(self.population.get_size()), weights=weights, k=self.population.get_size()
                )
            ], maximize=self.population.get_fitness_criteria()
        )
        # Crossover and mutation
        for i in range(new_population.get_size() // 2):
            if random.random() < self.crossover_probability:
                new_population[i].crossover(new_population[i + 1])
            else:
                if random.random() < self.mutation_probability:
                    new_population[i].mutate()
                if random.random() < self.mutation_probability:
                    new_population[i + 1].mutate()
        self.population = new_population


class CrowSearchAlgorithm(object):
    """Evolve a population iteratively to find better
    individuals on each generation. If elitism is set, the
    fittest individual of a generation will be part of the
    next one.
    """

    def __init__(self, flock, tournament_size=5):
        self.flock = flock
        self.x_train, self.y_train = self.flock.get_data()
        self.tournament_size = tournament_size
        self.iteration = 1
        self.max_iterations=0
        self.explored=[crow.get_location() for crow in self.flock]
        unique=[]
        for location in self.explored:
            if location not in unique:
                unique.append(location)
        print(self.explored)
        print(unique)
        print("Flock Size",len(self.explored),";","Unique Crows",len(unique))
    def get_flock_type(self):
        return self.flock.__class__

    def run(self, max_iterations,exp_no):
        print("\nStarting Crow Search Algorithm...")
        self.max_iterations=max_iterations
        while self.iteration <= max_iterations:
            exp_col.update_one({"no":exp_no},{"$push":{"iterations":[]}})
            self.release_flock()
            self.iteration += 1

    def release_flock(self):
        if self.flock.get_size() < self.tournament_size:
            raise ValueError("Flock size is smaller than tournament size.")
        print("\nRunning iteration #{}...".format(self.iteration))
        fittest = self.flock.get_fittest()
        print("\nBest performance is {:.8f} by Crow {} on the location :".format(fittest.get_best_fitness(),fittest.get_id()),fittest.get_memory())
        # print(fittest.get_memory())
        # print("Fitness value is: {:.8f}\n".format(fittest.get_best_fitness()))
        if self.iteration <self.max_iterations:
            for i,_ in enumerate(self.flock):
                crow=self.flock[i]
                while crow.get_location() in self.explored:
                    crow.follow(self.tournament_select(crow))
                self.explored.append(crow.get_location())
            # new_locations = [crow.get_location() for crow in self.flock]
            # repeated=[]
            # for location in new_locations:
            #     if location in self.explored:
            #         repeated.append(location)
            # print("Flock Size", len(new_locations), ";", "Repeated Crows", len(repeated))
            # for r in repeated:
            #     print(r)


    def tournament_select(self,crow):
        individual_list=[self.flock[i] for i in random.sample(range(self.flock.get_size()), self.tournament_size) if crow.get_id() !=self.flock[i].get_id()]
        target=max(individual_list, key=operator.methodcaller('get_best_fitness'))
        return target
#!/usr/bin/env python Genetic algorithm class Evolve a population iteratively to find better     individuals on each generation. If elitism is set, the     fittest individual of a generation will be part of the     next one. #{}...".format(self.generation)) Simpler genetic algorithm used in the Genetic CNN paper. #{}...".format(self.generation)) # Russian roulette selection # Crossover and mutation Evolve a population iteratively to find better     individuals on each generation. If elitism is set, the     fittest individual of a generation will be part of the     next one. #{}...".format(self.iteration)) # print(fittest.get_memory()) # print("Fitness value is: {:.8f}\n".format(fittest.get_best_fitness())) # new_locations = [crow.get_location() for crow in self.flock] # repeated=[] # for location in new_locations: #     if location in self.explored: #         repeated.append(location) # print("Flock Size", len(new_locations), ";", "Repeated Crows", len(repeated)) # for r in repeated: #     print(r)