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Published byJayla Sutterfield Modified about 1 year ago

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1 Exact and heuristics algorithms

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

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3 Genetic algorithm: Chromosomes Chromosomes are used to code information. Example: 3 warehouses, 5 clients W1W2W3C1C2C3C4C5 10132112

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

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

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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 W1W2W3C1C2C3C4C5 10132112 W1W2W3C1C2C3C4C5 10132112 Father Mother

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7 Genetic algorithm: Operators Crossover Child 1 Child 2 W1W2W3C1C2C3C4C5 10132112 W1W2W3C1C2C3C4C5 10133122 W1W2W3C1C2C3C4C5 10133112 W1W2W3C1C2C3C4C5 10132122 Father Mother

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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 W1W2W3C1C2C3C4C5 10132112 W1W2W3C1C2C3C4C5 00132112 Current New

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

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