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Genetic Algorithms

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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

- subset of evolutionary computation
- generic, population based optimization algorithms
- uses aspects of biology

- Gene = smallest unit of data
- represented in binary

- Genome = string of genes
- Genome pool = set of genomes
- represents the population

- Mutation
- Crossover
- Inheritance

- Loops through every gene of every member
- Two main classes:
- no change
- mutable

- 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)

- Opponent adaptation
- Towers of Reus

- Created in 2010 for Zerg
- user inputs goal and the app generates the build order

- 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

- 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

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

http://rednuht.org/genetic_cars_2/

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