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Genetic Algorithms
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  1. Genetic Algorithms By: Anna Scheuler and Aaron Smittle

  2. What is it? • appeared in the 1950s and 1960s • used to find approximations in search problems • use principles of natural selection to find an optimized solution • part of evolutionary algorithms

  3. Evolutionary Algorithms • subset of evolutionary computation • generic, population based optimization algorithms • uses aspects of biology

  4. Biology → Genetic Algorithms • Gene = smallest unit of data • represented in binary • Genome = string of genes • Genome pool = set of genomes • represents the population • Mutation • Crossover • Inheritance

  5. The Fitness Function • Loops through every gene of every member • Two main classes: • no change • mutable

  6. The Algorithm • Randomly generate an initial population • Run fitness function • Define parameters for “strong” members • Create new generation • Introduce mutation • Repeat A simple algorithm runs in O(g*n*m)

  7. GAs and Gaming • Opponent adaptation • Towers of Reus

  8. Star Craft’s Evolution Chamber • Created in 2010 for Zerg • user inputs goal and the app generates the build order

  9. Card Problem Example • There are 10 cards numbered 1-10. • There must be two piles • The sum of the first pile must be as close as possible to 36 • The product of the second pile must be as close as possible to 360

  10. Card Problem cont. • Genome is the way the cards are divided • Algorithm begins by picking two genomes at random • They are compared with Fitness test • Copy winner into loser and mutate with random probability at each gene

  11. Card Problem Fitness Function

  12. Card Problem • This problem used a Microbial GA • This type of genetic algorithm features ‘free’ elitism • Relatively simple core code

  13. An example http://rednuht.org/genetic_cars_2/

  14. Issues • The fitness function must be carefully written • Members can get lost • Population can converge with similar traits

  15. Questions?