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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.

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Genetic algorithms

Genetic Algorithms

By: Anna Scheuler and Aaron Smittle


What is it
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
Evolutionary Algorithms

  • subset of evolutionary computation

  • generic, population based optimization algorithms

  • uses aspects of biology


Biology genetic algorithms
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
The Fitness Function

  • Loops through every gene of every member

  • Two main classes:

    • no change

    • mutable


The algorithm
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
GAs and Gaming

  • Opponent adaptation

  • Towers of Reus


Star craft s evolution chamber
Star Craft’s Evolution Chamber

  • Created in 2010 for Zerg

  • user inputs goal and the app generates the build order


Card problem example
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
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
Card Problem

  • This problem used a Microbial GA

    • This type of genetic algorithm features ‘free’ elitism

    • Relatively simple core code


An example
An example

http://rednuht.org/genetic_cars_2/


Issues
Issues

  • The fitness function must be carefully written

  • Members can get lost

  • Population can converge with similar traits



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