# Exact and heuristics algorithms

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Exact and heuristics algorithms

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

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

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

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

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

Genetic algorithm: Operators
W1 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

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

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