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##### Genetic Algorithms

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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**• 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**• Opponent adaptation • 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