Genetic Algorithms

By: Anna Scheuler and Aaron Smittle

● 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?

• subset of evolutionary computation

• generic, population based optimization algorithms

• uses aspects of biology

Evolutionary Algorithms

• Gene = smallest unit of datao represented in binary

• Genome = string of genes

• Genome pool = set of genomeso represents the population

• Mutation

• Crossover

• Inheritance

Biology → Genetic Algorithms

• Loops through every gene of every member

• Two main classes:o no changeo mutable

The Fitness Function

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

• Opponent adaptation

• Towers of Reus

GAs and Gaming

• Created in 2010 for Zerg

• user inputs goal and the app generates the build order

Star Craft’s Evolution Chamber

● 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

● 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.

Card Problem Fitness Function

● This problem used a Microbial GA○ This type of genetic algorithm features ‘free’

elitism○ Relatively simple core code

Card Problem

http://rednuht.org/genetic_cars_2/

An example

• The fitness function must be carefully written

• Members can get lost

• Population can converge with similar traits

Issues

Questions?