This presentation is the property of its rightful owner.
1 / 15

# Genetic Algorithms PowerPoint PPT Presentation

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.

Genetic Algorithms

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

## 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

• 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

• 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