# Genetic Algorithms

Sablier
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## Algorithm

### Algorithm overview

1. 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.
2. Evaluation Each individual is scored on its fitting to the problem. This is done in the beginning of the selection.
3. 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.
4. 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
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