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- Create the base population
We create a random initial population. Each individual is is defined by its genetic material.
We create a new individual with the previously declared
- Evaluation Each individual is scored on its fitting to the problem. This is done in the beginning of the selection.
Each individual has a chance to be retained proportional to the way it fits the problem.
We only keep the selected individuals returned by the
- Crossover / reproduction Random couples are formed in the selected population. Each couple produces a new individual. The number of individuals in the population can either be constant or vary over time.
On each reproduction :
- Crossover The genetic material of a child is a combination of the parents' (generally 50% of each parent's genetic material). Once the parents have been chosen the ``crossover(parent1, parent2)` function allows the creation of the child.
Probability : from 0.1% to 1%
Each child have a chance to have a randomly modified gene thanks to the
is_answer(chromosome) function checks if the individual is solution to the problem (100% score).
If there is no solution, we go to the next generation (phase 2).
The goal of this exercise is to find the secret sentence using a genetic algorithm and the tools we created earlier.
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from answer import is_answer, get_mean_score
# You can redefine these functions with the ones you wrote previously.
# Another implementation is provided here.
from encoding import create_chromosome
from tools import selection, crossover, mutation
def create_population(pop_size, chrom_size):
# use the previously defined create_chromosome(size) function
# TODO: create the base population
chrom = create_chromosome(chrom_size)
# use the selection(population) function created on exercise 2
select = selection(population)
# As long as we need individuals in the new population, fill it with children
children = 
# TODO: implement the reproduction
while len(children) < ???:
parent1 = ??? # randomly selected
parent2 = ??? # randomly selected
# use the crossover(parent1, parent2) function created on exercise 2
child = crossover(parent1, parent2)
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