def random_selection(population, fitness_fn):
"""
Compute fitness of each in population according to fitness_fn and add up
the total. Then choose 2 from sequence based on percentage contribution to
total fitness of population
Return selected variable which holds two individuals that were chosen as
the mother and the father
"""
# Python sets are randomly ordered. Since we traverse the set twice, we
# want to do it in the same order. So let's convert it temporarily to a
# list.
ordered_population = list(population)
fitness = []
sumFit = 0
for i in ordered_population:
fitAmount = fitness_fn_positive(i)
sumFit += fitAmount
for f in range(fitAmount):
fitness.append(i)
returnList = []
for k in range(2):
returnList.append(fitness[random.randint(0, sumFit - 1)])
return returnList
def main():
minimal_fitness = 28
# Curly brackets also creates a set, if there isn't a colon to indicate a dictionary
initial_population = get_initial_population(8, 8)
initial_population = calculate_fitness(initial_population, fitness_fn_positive)
fittest = genetic_algorithm(initial_population, fitness_fn_positive, minimal_fitness)
print('Fittest Individual: ' + str(fittest) + " with fitness " + str(fitness_fn_positive(fittest)))
def fitness_function(individual):
'''
Computes the decimal value of the individual
Return the fitness level of the individual
Explanation:
enumerate(list) returns a list of pairs (position, element):
enumerate((4, 6, 2, 8)) -> [(0, 4), (1, 6), (2, 2), (3, 8)]
enumerate(reversed((1, 1, 0))) -> [(0, 0), (1, 1), (2, 1)]
'''
fitness = queens_fitness.fitness_fn_positive(individual)
return fitness
def fitness_function(individual):
return (queens_fitness.fitness_fn_positive(individual))