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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

Genetic Algorithms

Vida Movahedi

November 2006

contents
Contents
  • What are Genetic Algorithms?
  • From Biology …
  • Evolution
  • … To Genetic Algorithms
  • Demo
what are genetic algorithms
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
from biology

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
evolution
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
initial population
Initial Population
  • Representation of possible solutions as chromosomes
    • Binary
    • Real
    • etc.
  • Random initial population
  • If not random  stuck in local optima
recombination crossover
Recombination (crossover)
  • Random crossover points
  • Inheriting genes from one parent
mutation
Mutation
  • Random Mutation Point
  • Changing gene value to a random value
to genetic algorithms
… 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

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