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Genetic Algorithms

Genetic Algorithms. Vida Movahedi November 2006. Contents. What are Genetic Algorithms? From Biology … Evolution … To Genetic Algorithms Demo. What are Genetic Algorithms?. A method of solving Optimization Problems Exponentially large set of solutions Easy to compute cost or value

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Genetic Algorithms

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

  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

  11. Example • http://www.rennard.org/alife/english/gavgb.html

  12. References • [1] Hue, Xavier (1997), “Genetic Algorithms for Optimisation: Background and Applications”, http://www.epcc.ed.ac.uk/overview/publications/training_material/tech_watch/97_tw/techwatch-ga/ • [2] Whitely, Darell (1995), “A Genetic Algorithm Tutorial”, http://samizdat.mines.edu/ga_tutorial/

  13. Questions?

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