<gh_stars>0
import math
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# the following code is provided and doesn't need to be changed.

def selection(population,number_of_parameters):
    '''
    Rank the individuals of the population 
    '''
    # we implement rank selection here, i.e. the individuals with the highest fitness go to the next stage
    # in the current implementation that simply means that we have to sort the population according to fitness 
    #(the last column)

    population = population[population[:,number_of_parameters].argsort()] # this sorts the array with the individuals with the lowest fitness at the top (remember
    # we want to minimize our function)
    
    return population

def evaluateFitness(population,capacity,number_of_parameters):
    '''
    Evaluate the fitness of  all individuals
    '''
    for i in range(capacity):
        #print('evaluation:',i)
        # take the parameters of the i-th individual and evaluate the fitness, save the fitness value in the last column 
        population[i,number_of_parameters] = fitness_function(population[i,0:number_of_parameters])
    
        
    return population

def visualize(population,size):
    '''
    Visualize each individual of the population in a 3D plot. 
    The individual with the best fitness is marked with a red cross
    '''
    # Note: only works for two parameters (but that's all we need for this exercise)
    fig = plt.figure()
    ax3d = fig.add_subplot(111,projection='3d')
    ax3d.scatter(population[1:size,0],population[1:size,1],population[1:size,2],c='k',marker='o') # plot all individuals in black with a 'o' marker
    ax3d.scatter(population[0,0],population[0,1],population[0,2],c='r',marker='x') # plot the best individual in red with a 'x' marker
    plt.show()

def fitness_function(params):# the function to optimize
    """
    Computes the "fitness" of a set of parameters. The lower the fitness, the better.
    """
    x = params[0]
    y = params[1]
    fitness = (3*(x**2))+(4*(y**2)) # here you should replace None with the code for the function of x and y as above
    return fitness 

###### don't change the code below

assert fitness_function([3,4]) == 91, 'please check your implementation'
assert fitness_function([3,2]) == 43, 'please check your implementation'
assert fitness_function([5,23]) == 2191, 'please check your implementation'


def mutation(population, size, r_mutation, p_mutation, s_mutation, number_of_parameters):
    '''
    mutation - mutate a random subset of the population
    Parameters:
    population - the population from the current generation
    (We simply represent the parameters as a numpy array, and initialize it with random numbers
    the first size rows represent the actual population, the rest gets filled during mutation and crossover
    the first number_of_parameters columns contain the parameters, the last column contains the fitness value)
    size - the size of the population
    r_mutation - the percentage of individuals from the population that undergo mutation
    p_mutation - the probability of a mutation of a single parameter of an individual
    s_mutation - the strength of the mutation
    number_of_parameters - the number of parameters each individual has
    '''
    # choose a random subset of the population which will undergo mutation
    cutoff = int(size*r_mutation) # this is the number of individuals that will undergo mutation
    indices = np.arange(size)
    
    np.random.shuffle(indices)
    mutation_set = indices[:cutoff] # these are the indices of the randomly chosen individuals that will undergo
                                    # mutation

    inc = 0
    for i in mutation_set: # loop through all individuals that undergo mutation
        
        for j in range(number_of_parameters): # loop through all parameters of each individual
            p = np.random.rand() # integer in interval 0,1
            if p <= p_mutation: # if integer > p_mutation perform mutation
                r = (1*s_mutation) * np.random.random_sample() - (0.5*s_mutation) # generate number in range given
                population[i,j] = population[i,j] + r # parameter value + r
                population[size+inc,1] = r # store mutated parameter into population    
        population[size+inc,0] = population[size+inc,1] # store mutated individual into population
        inc += 1        
 
    return population


###### don't change the code below
population = np.random.random_sample((25,3)) 
pop = np.array(population) 
mutation_population = mutation(population,10,1.0,1.0,1.0,2)


            
assert mutation_population[10,0] != pop[10,0], 'please check your implementation'
assert mutation_population[10,1] != pop[10,1], 'please check your implementation'
assert mutation_population[11,0] != pop[11,0], 'please check your implementation'
assert mutation_population[11,1] != pop[11,1], 'please check your implementation'
assert mutation_population[12,0] != pop[12,0], 'please check your implementation'
assert mutation_population[12,1] != pop[12,1], 'please check your implementation'
assert mutation_population[13,0] != pop[13,0], 'please check your implementation'
assert mutation_population[13,1] != pop[13,1], 'please check your implementation'
assert mutation_population[14,0] != pop[14,0], 'please check your implementation'
assert mutation_population[14,1] != pop[14,1], 'please check your implementation'

def crossover(population,size,r_crossover,r_mutation,number_of_parameters):
    '''
     crossover - Choose a random subset of the population, select parent pairs and produce offspring
    Parameters:
    population - the population from the current generation
    (We simply represent the parameters as a numpy array, and initialize it with random numbers
    the first size rows represent the actual population, the rest gets filled during mutation and crossover
    the first number_of_parameters columns contain the parameters, the last column contains the fitness value)
    size - the size of the population)
    r_crossover - the percentage of individuals from the population that serve as parents for crossover
    r_mutation - the percentage of individuals from the population that undergo mutation (needed in order
    to correctly store the newly produced offspring!)
    number_of_parameters - the number of parameters each individual has
    '''
    # At the moment, when we do the crossover, the population is already sorted according to fitness values!
    cutoff = int(size*r_crossover) # this is the number of individuals that will undergo crossover
    crossover_set = np.arange(cutoff) # these are the indices of the self.r_crossover % individuals that will undergo crossover
    a = int((size * r_mutation) + size) # continuation from mutated individuals

    
    n = 0
    while n<cutoff:
        individual_a = np.random.choice(crossover_set) # randomly choose the first individual
        crossover_set = [x for x in crossover_set if x not in [individual_a]] # remove the individual from the set
        individual_b = np.random.choice(crossover_set) # randomly choose the second individual from the remaining set
        crossover_set = [x for x in crossover_set if x not in [individual_b]] # remove the individual from the set
        # perform the actual crossover
        # here you should place your code for task 3); 
        # the comments in the following lines give you a hint of what you are supposed to do
        for j in range(number_of_parameters):
            #print(j)
            r = np.random.rand()
            if r < 0.5:
                population[a,j] = population[individual_a,j]
            else:
                population[a,j] = population[individual_b,j]	
        n = n+2# increase counter by 2, since we have chosen 2 individuals
        a = a + 1
    return population

###### don't change the code below
population = np.random.random_sample((25,3)) 
pop = np.array(population) 
crossover_population = crossover(population,10,1.0,1.0,2)


assert crossover_population[20,0] != pop[20,0], 'please check your implementation'
assert crossover_population[20,1] != pop[20,1], 'please check your implementation'
assert crossover_population[21,0] != pop[21,0], 'please check your implementation'
assert crossover_population[21,1] != pop[21,1], 'please check your implementation'
assert crossover_population[22,0] != pop[22,0], 'please check your implementation'
assert crossover_population[22,1] != pop[22,1], 'please check your implementation'
assert crossover_population[23,0] != pop[23,0], 'please check your implementation'
assert crossover_population[23,1] != pop[23,1], 'please check your implementation'

def evolve(population,size,r_mutation,p_mutation,s_mutation,r_crossover,capacity,number_of_parameters,number_of_generations):
    '''
    evolve - evolves the genetic algorithm for the specified number of generations
    Parameters:
    population - the population from the current generation
    size - the size of the population
    r_mutation - the percentage of individuals from the population that undergo mutation
    p_mutation - the probability of a mutation of a single parameter of an individual
    s_mutation - the strength of the mutation
    r_crossover - the percentage of individuals from the population that serve as parents for the crossover
    capacity - the overall size of the population array (includes the actual population, the newly mutated individuals
    and the newly produced offspring)
    number_of_parameters - the number of parameters each individual has
    number_of_generations - the number of generations the genetic algorithm will be evolved
    '''
    
    for i in range(number_of_generations):
        print(i) # print the current generation number
        # here you should place your code for task 4)
        # decide in which order the functions have to be called
        # call them and give them the correct parameters as arguments
        evaluateFitness(population, capacity, number_of_parameters)
        population = selection(population, number_of_parameters)
        crossover(population,size,r_crossover,r_mutation,number_of_parameters )
        mutation(population, size, r_mutation, p_mutation, s_mutation, number_of_parameters)
        
           
    
    #visualize(population,size)
    return population

    




size = 100 # the size of the population
number_of_parameters = 2 # the number of parameters the function takes
number_of_generations = 20 # number of generations that are executed
p_mutation  = 0.5 # the probability of a mutation
r_mutation  = 0.2 # the percentage of individuals that undergo mutation
s_mutation  = 1.25 # the mutation strength
r_crossover = 0.4 # the percentage of individuals that undergo crossover

capacity = size + int(size*r_mutation) + int(size*r_crossover/2.0) # this is the size of the population after crossover and selection
print("capactiy",capacity)
# We simply represent the parameters as a numpy array, and initialize it with random numbers
# the first size rows represent the actual population, the rest gets filled during mutation and crossover
# the first number_of_parameters columns contain the parameters, the last column contains the fitness value
population = np.random.random_sample((capacity,number_of_parameters+1)) 

final_population = evolve(population,size,r_mutation,p_mutation,s_mutation,r_crossover,capacity,number_of_parameters,number_of_generations)


###### don't change the code below

print(final_population[0,2])
assert final_population[0,2] < 0.001, 'please check your implementation'
assert final_population[1,2] < 0.001, 'please check your implementation'
assert final_population[2,2] < 0.001, 'please check your implementation'
assert final_population[3,2] < 0.001, 'please check your implementation'
assert final_population[4,2] < 0.001, 'please check your implementation'


import math
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# the following code is provided and doesn't need to be changed.

def selection(population,number_of_parameters):
    '''
    Rank the individuals of the population 
    '''
    # we implement rank selection here, i.e. the individuals with the highest fitness go to the next stage
    # in the current implementation that simply means that we have to sort the population according to fitness 
    #(the last column)

    population = population[population[:,number_of_parameters].argsort()] # this sorts the array with the individuals with the lowest fitness at the top (remember
    # we want to minimize our function)
    
    return population

def evaluateFitness(population,capacity,number_of_parameters):
    '''
    Evaluate the fitness of  all individuals
    '''
    for i in range(capacity):
        #print('evaluation:',i)
        # take the parameters of the i-th individual and evaluate the fitness, save the fitness value in the last column 
        population[i,number_of_parameters] = fitness_function(population[i,0:number_of_parameters])
    
        
    return population

def visualize(population,size):
    '''
    Visualize each individual of the population in a 3D plot. 
    The individual with the best fitness is marked with a red cross
    '''
    # Note: only works for two parameters (but that's all we need for this exercise)
    fig = plt.figure()
    ax3d = fig.add_subplot(111,projection='3d')
    ax3d.scatter(population[1:size,0],population[1:size,1],population[1:size,2],c='k',marker='o') # plot all individuals in black with a 'o' marker
    ax3d.scatter(population[0,0],population[0,1],population[0,2],c='r',marker='x') # plot the best individual in red with a 'x' marker
    plt.show()

def fitness_function(params):# the function to optimize
    """
    Computes the "fitness" of a set of parameters. The lower the fitness, the better.
    """
    x = params[0]
    y = params[1]
    fitness = (3*(x**2))+(4*(y**2)) # here you should replace None with the code for the function of x and y as above
    return fitness 

###### don't change the code below

assert fitness_function([3,4]) == 91, 'please check your implementation'
assert fitness_function([3,2]) == 43, 'please check your implementation'
assert fitness_function([5,23]) == 2191, 'please check your implementation'


def mutation(population, size, r_mutation, p_mutation, s_mutation, number_of_parameters):
    '''
    mutation - mutate a random subset of the population
    Parameters:
    population - the population from the current generation
    (We simply represent the parameters as a numpy array, and initialize it with random numbers
    the first size rows represent the actual population, the rest gets filled during mutation and crossover
    the first number_of_parameters columns contain the parameters, the last column contains the fitness value)
    size - the size of the population
    r_mutation - the percentage of individuals from the population that undergo mutation
    p_mutation - the probability of a mutation of a single parameter of an individual
    s_mutation - the strength of the mutation
    number_of_parameters - the number of parameters each individual has
    '''
    # choose a random subset of the population which will undergo mutation
    cutoff = int(size*r_mutation) # this is the number of individuals that will undergo mutation
    indices = np.arange(size)
    
    np.random.shuffle(indices)
    mutation_set = indices[:cutoff] # these are the indices of the randomly chosen individuals that will undergo
                                    # mutation

    inc = 0
    for i in mutation_set: # loop through all individuals that undergo mutation
        
        for j in range(number_of_parameters): # loop through all parameters of each individual
            p = np.random.rand() # integer in interval 0,1
            if p <= p_mutation: # if integer > p_mutation perform mutation
                r = (1*s_mutation) * np.random.random_sample() - (0.5*s_mutation) # generate number in range given
                population[i,j] = population[i,j] + r # parameter value + r
                population[size+inc,1] = r # store mutated parameter into population    
        population[size+inc,0] = population[size+inc,1] # store mutated individual into population
        inc += 1        
 
    return population


###### don't change the code below
population = np.random.random_sample((25,3)) 
pop = np.array(population) 
mutation_population = mutation(population,10,1.0,1.0,1.0,2)


            
assert mutation_population[10,0] != pop[10,0], 'please check your implementation'
assert mutation_population[10,1] != pop[10,1], 'please check your implementation'
assert mutation_population[11,0] != pop[11,0], 'please check your implementation'
assert mutation_population[11,1] != pop[11,1], 'please check your implementation'
assert mutation_population[12,0] != pop[12,0], 'please check your implementation'
assert mutation_population[12,1] != pop[12,1], 'please check your implementation'
assert mutation_population[13,0] != pop[13,0], 'please check your implementation'
assert mutation_population[13,1] != pop[13,1], 'please check your implementation'
assert mutation_population[14,0] != pop[14,0], 'please check your implementation'
assert mutation_population[14,1] != pop[14,1], 'please check your implementation'

def crossover(population,size,r_crossover,r_mutation,number_of_parameters):
    '''
     crossover - Choose a random subset of the population, select parent pairs and produce offspring
    Parameters:
    population - the population from the current generation
    (We simply represent the parameters as a numpy array, and initialize it with random numbers
    the first size rows represent the actual population, the rest gets filled during mutation and crossover
    the first number_of_parameters columns contain the parameters, the last column contains the fitness value)
    size - the size of the population)
    r_crossover - the percentage of individuals from the population that serve as parents for crossover
    r_mutation - the percentage of individuals from the population that undergo mutation (needed in order
    to correctly store the newly produced offspring!)
    number_of_parameters - the number of parameters each individual has
    '''
    # At the moment, when we do the crossover, the population is already sorted according to fitness values!
    cutoff = int(size*r_crossover) # this is the number of individuals that will undergo crossover
    crossover_set = np.arange(cutoff) # these are the indices of the self.r_crossover % individuals that will undergo crossover
    a = int((size * r_mutation) + size) # continuation from mutated individuals

    
    n = 0
    while n<cutoff:
        individual_a = np.random.choice(crossover_set) # randomly choose the first individual
        crossover_set = [x for x in crossover_set if x not in [individual_a]] # remove the individual from the set
        individual_b = np.random.choice(crossover_set) # randomly choose the second individual from the remaining set
        crossover_set = [x for x in crossover_set if x not in [individual_b]] # remove the individual from the set
        # perform the actual crossover
        # here you should place your code for task 3); 
        # the comments in the following lines give you a hint of what you are supposed to do
        for j in range(number_of_parameters):
            #print(j)
            r = np.random.rand()
            if r < 0.5:
                population[a,j] = population[individual_a,j]
            else:
                population[a,j] = population[individual_b,j]	
        n = n+2# increase counter by 2, since we have chosen 2 individuals
        a = a + 1
    return population

###### don't change the code below
population = np.random.random_sample((25,3)) 
pop = np.array(population) 
crossover_population = crossover(population,10,1.0,1.0,2)


assert crossover_population[20,0] != pop[20,0], 'please check your implementation'
assert crossover_population[20,1] != pop[20,1], 'please check your implementation'
assert crossover_population[21,0] != pop[21,0], 'please check your implementation'
assert crossover_population[21,1] != pop[21,1], 'please check your implementation'
assert crossover_population[22,0] != pop[22,0], 'please check your implementation'
assert crossover_population[22,1] != pop[22,1], 'please check your implementation'
assert crossover_population[23,0] != pop[23,0], 'please check your implementation'
assert crossover_population[23,1] != pop[23,1], 'please check your implementation'

def evolve(population,size,r_mutation,p_mutation,s_mutation,r_crossover,capacity,number_of_parameters,number_of_generations):
    '''
    evolve - evolves the genetic algorithm for the specified number of generations
    Parameters:
    population - the population from the current generation
    size - the size of the population
    r_mutation - the percentage of individuals from the population that undergo mutation
    p_mutation - the probability of a mutation of a single parameter of an individual
    s_mutation - the strength of the mutation
    r_crossover - the percentage of individuals from the population that serve as parents for the crossover
    capacity - the overall size of the population array (includes the actual population, the newly mutated individuals
    and the newly produced offspring)
    number_of_parameters - the number of parameters each individual has
    number_of_generations - the number of generations the genetic algorithm will be evolved
    '''
    
    for i in range(number_of_generations):
        print(i) # print the current generation number
        # here you should place your code for task 4)
        # decide in which order the functions have to be called
        # call them and give them the correct parameters as arguments
        evaluateFitness(population, capacity, number_of_parameters)
        population = selection(population, number_of_parameters)
        crossover(population,size,r_crossover,r_mutation,number_of_parameters )
        mutation(population, size, r_mutation, p_mutation, s_mutation, number_of_parameters)
        
           
    
    #visualize(population,size)
    return population

    




size = 100 # the size of the population
number_of_parameters = 2 # the number of parameters the function takes
number_of_generations = 20 # number of generations that are executed
p_mutation  = 0.5 # the probability of a mutation
r_mutation  = 0.2 # the percentage of individuals that undergo mutation
s_mutation  = 1.25 # the mutation strength
r_crossover = 0.4 # the percentage of individuals that undergo crossover

capacity = size + int(size*r_mutation) + int(size*r_crossover/2.0) # this is the size of the population after crossover and selection
print("capactiy",capacity)
# We simply represent the parameters as a numpy array, and initialize it with random numbers
# the first size rows represent the actual population, the rest gets filled during mutation and crossover
# the first number_of_parameters columns contain the parameters, the last column contains the fitness value
population = np.random.random_sample((capacity,number_of_parameters+1)) 

final_population = evolve(population,size,r_mutation,p_mutation,s_mutation,r_crossover,capacity,number_of_parameters,number_of_generations)


###### don't change the code below

print(final_population[0,2])
assert final_population[0,2] < 0.001, 'please check your implementation'
assert final_population[1,2] < 0.001, 'please check your implementation'
assert final_population[2,2] < 0.001, 'please check your implementation'
assert final_population[3,2] < 0.001, 'please check your implementation'
assert final_population[4,2] < 0.001, 'please check your implementation'
# the following code is provided and doesn't need to be changed. Rank the individuals of the population # we implement rank selection here, i.e. the individuals with the highest fitness go to the next stage # in the current implementation that simply means that we have to sort the population according to fitness #(the last column) # this sorts the array with the individuals with the lowest fitness at the top (remember # we want to minimize our function) Evaluate the fitness of  all individuals #print('evaluation:',i) # take the parameters of the i-th individual and evaluate the fitness, save the fitness value in the last column Visualize each individual of the population in a 3D plot. 
     The individual with the best fitness is marked with a red cross # Note: only works for two parameters (but that's all we need for this exercise) # plot all individuals in black with a 'o' marker # plot the best individual in red with a 'x' marker # the function to optimize Computes the "fitness" of a set of parameters. The lower the fitness, the better. # here you should replace None with the code for the function of x and y as above ###### don't change the code below mutation - mutate a random subset of the population
     Parameters:
     population - the population from the current generation
     (We simply represent the parameters as a numpy array, and initialize it with random numbers
     the first size rows represent the actual population, the rest gets filled during mutation and crossover
     the first number_of_parameters columns contain the parameters, the last column contains the fitness value)
     size - the size of the population
     r_mutation - the percentage of individuals from the population that undergo mutation
     p_mutation - the probability of a mutation of a single parameter of an individual
     s_mutation - the strength of the mutation
     number_of_parameters - the number of parameters each individual has # choose a random subset of the population which will undergo mutation # this is the number of individuals that will undergo mutation # these are the indices of the randomly chosen individuals that will undergo # mutation # loop through all individuals that undergo mutation # loop through all parameters of each individual # integer in interval 0,1 # if integer > p_mutation perform mutation # generate number in range given # parameter value + r # store mutated parameter into population # store mutated individual into population ###### don't change the code below crossover - Choose a random subset of the population, select parent pairs and produce offspring
     Parameters:
     population - the population from the current generation
     (We simply represent the parameters as a numpy array, and initialize it with random numbers
     the first size rows represent the actual population, the rest gets filled during mutation and crossover
     the first number_of_parameters columns contain the parameters, the last column contains the fitness value)
     size - the size of the population)
     r_crossover - the percentage of individuals from the population that serve as parents for crossover
     r_mutation - the percentage of individuals from the population that undergo mutation (needed in order
     to correctly store the newly produced offspring!)
     number_of_parameters - the number of parameters each individual has # At the moment, when we do the crossover, the population is already sorted according to fitness values! # this is the number of individuals that will undergo crossover # these are the indices of the self.r_crossover % individuals that will undergo crossover # continuation from mutated individuals # randomly choose the first individual # remove the individual from the set # randomly choose the second individual from the remaining set # remove the individual from the set # perform the actual crossover # here you should place your code for task 3); # the comments in the following lines give you a hint of what you are supposed to do #print(j) # increase counter by 2, since we have chosen 2 individuals ###### don't change the code below evolve - evolves the genetic algorithm for the specified number of generations
     Parameters:
     population - the population from the current generation
     size - the size of the population
     r_mutation - the percentage of individuals from the population that undergo mutation
     p_mutation - the probability of a mutation of a single parameter of an individual
     s_mutation - the strength of the mutation
     r_crossover - the percentage of individuals from the population that serve as parents for the crossover
     capacity - the overall size of the population array (includes the actual population, the newly mutated individuals
     and the newly produced offspring)
     number_of_parameters - the number of parameters each individual has
     number_of_generations - the number of generations the genetic algorithm will be evolved # print the current generation number # here you should place your code for task 4) # decide in which order the functions have to be called # call them and give them the correct parameters as arguments #visualize(population,size) # the size of the population # the number of parameters the function takes # number of generations that are executed # the probability of a mutation # the percentage of individuals that undergo mutation # the mutation strength # the percentage of individuals that undergo crossover # this is the size of the population after crossover and selection # We simply represent the parameters as a numpy array, and initialize it with random numbers # the first size rows represent the actual population, the rest gets filled during mutation and crossover # the first number_of_parameters columns contain the parameters, the last column contains the fitness value ###### don't change the code below