Editing Genetic algorithm (section)

== External links ==

=== Resources ===
* [https://web.archive.org/web/20160303215222/http://www.geneticprogramming.com/ga/index.htm] Provides a list of resources in the genetic algorithms field
* [https://www.staracle.com/general/evolutionaryAlgorithms.php An Overview of the History and Flavors of Evolutionary Algorithms]

=== Tutorials ===
* [https://www2.econ.iastate.edu/tesfatsi/holland.gaintro.htm Genetic Algorithms - Computer programs that "evolve" in ways that resemble natural selection can solve complex problems even their creators do not fully understand] An excellent introduction to GA by John Holland and with an application to the Prisoner's Dilemma
* [http://www.i4ai.org/EA-demo/ An online interactive Genetic Algorithm tutorial for a reader to practise or learn how a GA works]: Learn step by step or watch global convergence in batch, change the population size, crossover rates/bounds, mutation rates/bounds and selection mechanisms, and add constraints.
* [https://web.archive.org/web/20130615042000/http://samizdat.mines.edu/ga_tutorial/ga_tutorial.ps A Genetic Algorithm Tutorial by Darrell Whitley Computer Science Department Colorado State University] An excellent tutorial with much theory
* [http://cs.gmu.edu/~sean/book/metaheuristics/ "Essentials of Metaheuristics"], 2009 (225 p). Free open text by Sean Luke.
* [http://www.it-weise.de/projects/book.pdf Global Optimization Algorithms &ndash; Theory and Application] {{Webarchive|url=https://web.archive.org/web/20080911075107/http://www.it-weise.de/projects/book.pdf |date=11 September 2008 }}
* [https://mpatacchiola.github.io/blog/2017/03/14/dissecting-reinforcement-learning-5.html Genetic Algorithms in Python] Tutorial with the intuition behind GAs and Python implementation.
* [http://www-personal.umich.edu/~axe/research/Evolving.pdf Genetic Algorithms evolves to solve the prisoner's dilemma.] Written by Robert Axelrod.

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