Algorithm
Algorithm overview
- 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
create_chromosome(size)function. - Evaluation Each individual is scored on its fitting to the problem. This is done in the beginning of the selection.
- 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
selection(population)function. - 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.
- Mutation
Probability : from 0.1% to 1%
Each child have a chance to have a randomly modified gene thanks to the
mutation(chromosome)function.
Finally, 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.
Genetic algorithm
