Genetic Algorithms

Genetic Algorithms By: Anna Scheuler and Aaron Smittle

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

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

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

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

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)

GAs and Gaming • Opponent adaptation • Towers of Reus

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

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

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

Card Problem Fitness Function

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

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

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

Questions?

Genetic Algorithms