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Genetic Algorithms Vida Movahedi November 2006. Contents What are Genetic Algorithms? From Biology … Evolution … To Genetic Algorithms Demo.

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Presentation on theme: "Genetic Algorithms Vida Movahedi November 2006. Contents What are Genetic Algorithms? From Biology … Evolution … To Genetic Algorithms Demo."— Presentation transcript:

1 Genetic Algorithms Vida Movahedi November 2006

2 Contents What are Genetic Algorithms? From Biology … Evolution … To Genetic Algorithms Demo

3 What are Genetic Algorithms? A method of solving Optimization Problems –Exponentially large set of solutions –Easy to compute cost or value Search algorithm (looking for the optimum) Very similar to random search?! Population- based –We start with a set of possible solutions (initial population) and evolve it to get to the optimum –Also called Evolutionary Algorithms Based on evolution in biology

4 From Biology … Charles Darwin (1859) Natural selection, “survival of the fittest” Improvement of species Can we use the same idea to get an optimal solution?

5 Evolution To implement optimization as evolution, We need Mapping features to genes, showing each individual with a chromosome An initial population Have a function to measure fitness  same as what we want to optimize Implement and apply Reproduction Replace offspring in old generation Have an exit condition for looping over generations

6 Initial Population Representation of possible solutions as chromosomes –Binary –Real –etc. Random initial population If not random  stuck in local optima

7 Recombination (crossover) Random crossover points Inheriting genes from one parent

8 Mutation Random Mutation Point Changing gene value to a random value

9 … to Genetic Algorithms BEGIN /* genetic algorithm*/ Generate initial population ; Compute fitness of each individual ; LOOP Select individuals from old generations for mating ; Create offspring by applying recombination and/or mutation to the selected individuals ; Compute fitness of the new individuals ; Kill old individuals,insert offspring in new generation ; IF Population has converged THEN exit loop; END LOOP END

10 Simple Example

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14 Example htmlhttp://www.rennard.org/alife/english/gavgb. html

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16 References [1] Hue, Xavier (1997), “Genetic Algorithms for Optimisation: Background and Applications”, ions/training_material/tech_watch/97_tw/te chwatch-ga/ ions/training_material/tech_watch/97_tw/te chwatch-ga/ [2] Whitely, Darell (1995), “A Genetic Algorithm Tutorial”,

17 Questions?


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