1. Exact and heuristics algorithms

2. Exercice1 Model charging station deployement problem.
Program the Genetic algorithm to solve it.

3. Genetic algorithm: Chromosomes
Chromosomes are used to code information.
Example: 3 warehouses, 5 clients W1 W2 W3 C1 C2 C3 C4 C5 1 3 2

4. Genetic algorithm: Operators
Population
Select
Crossover
Mutation No
Final iteration
Recombination
Yes
Best solution

5. Genetic algorithm: Operators
Population
1- Randomly generate an initial population (random chromosomes)
2 -Compute and save the fitness (Objective function F) for each individual (chromosomes) in the current population
Select
3-Select some chromosomes from the population as an offspring individual:
Randomly
using stochastic method

6. Genetic algorithm: Operators
The crossover is done on a selected part of population (offspring) to create the basis of the next generation (exchange information).
This operator is applied with propability Pc
Crossover W1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Father W1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Mother

7. Genetic algorithm: OperatorsW1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Father
Crossover W1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Mother W1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Child 1 W1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Child 2

8. Genetic algorithm: Operators
This operation is a random change in the population. It modifies one or
more gene values in a chromosome to have a new chromosom value in the pool.
This operator is applied with propability Pm
Mutation W1 W2 W3 C1 C2 C3 C4 C5 1 3 2
Current
New W1 W2 W3 C1 C2 C3 C4 C5 1 3 2

9. Genetic algorithm: Operators
Recombination combines the chromosomes from the initial population and the new offspring chromosomes.
Recombination
Repeat a fixed number of iteration or until the solution converge to one solution (always with the best fitness) .
Final iteration