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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. - PowerPoint PPT Presentation
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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?
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