1. Genetic Algorithms By: Anna Scheuler and Aaron Smittle

2. ●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 What is it?

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

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

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

6. 1.Randomly generate an initial population 2.Run fitness function 3.Define parameters for “strong” members 4.Create new generation 5.Introduce mutation 6.Repeat A simple algorithm runs in O(g*n*m) The Algorithm

7. Opponent adaptation Towers of Reus GAs and Gaming

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

9. ●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 Card Problem Example

10. ●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 Card Problem cont.

11. Card Problem Fitness Function

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

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

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

15. Questions?