Genetic Algorithms: Difference between revisions

Latest revision as of 06:16, 9 May 2022

Advanced programming^[1]

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection^[2]

Please first review the characteristics of algorithms.
Please then review the characteristics of heuristics.

The basic pattern of genetic algorithms[edit]

A random set of solutions would be generated on the sample documents
And tested against the human labelling
Best fit solutions retained
New generation created by mutating/crossing
Algorithm repeated
Until a good fit obtained

Use of genetic algorithms[edit]

Some videos[edit]

Helpful resources[edit]

This article is a good place to get started
a good game: https://andymakes.itch.io/combat-genetics
a better game to help you understand genetic algorithms: https://david-birge-cotte.itch.io/evolution-sandbox

Terms associated with genetic algorithms[edit]

References[edit]

[1] ttp://www.flaticon.com/

[2] ttps://en.wikipedia.org/wiki/Genetic_algorith

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 [[file:computation.png|right|frame|Advanced programming<ref>http://www.flaticon.com/</ref>]]
+In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection<ref>https://en.wikipedia.org/wiki/Genetic_algorith</ref>
+* Please first review the characteristics of [[Algorithms|algorithms]].
+* Please then review the characteristics of [[Heuristics|heuristics]].
-== Start here to understand genetic algorithms ==
+== The basic pattern of genetic algorithms ==
+# A random set of solutions would be generated on the sample documents
+# And tested against the human labelling
+# Best fit solutions retained
+# New generation created by mutating/crossing
+# Algorithm repeated
+# Until a good fit obtained
-[[Brute force approach]]
+== Use of genetic algorithms ==
-[[Combinatorial optimization]]
+* [https://en.wikipedia.org/wiki/List_of_genetic_algorithm_applications Click here for a fairly good list]
-[[Computational intractability]]
+* [https://stackoverflow.com/questions/1538235/what-are-good-examples-of-genetic-algorithms-genetic-programming-solutions Click here for several good examples (scroll down)]
-[[Convergence]]
+* [https://www.brainz.org/15-real-world-applications-genetic-algorithms/ here are some short examples of GA]
-[[Crossover / crossover operator]]
-[[Elitism]]
-[[Exploration vs exploitation]]
-[[Fitness / fitness function / fitness landscape]]
-[[Heuristic]]
-[[Hill climbing]]
-[[Initialization parameters
-[[Local extrema]]
-[[Mating pool]]
-[[Mutation / mutation rate]]
-[[Novelty search]]
-[[Offspring]]
-[[Optimization]]
-[[Population]]
-[[Premature convergence]]
-[[Problem space]]
-[[Ranking]]
-[[Roulette wheel selection]]
-[[Selection strategy]]
-[[Simulated annealing]]
-[[Stochastic universal sampling]]
-[[Termination condition]]
-[[Tour]]
-[[Tournament selection]]
-[[Truncation selection]]
+== Some videos ==
+<html>
+<iframe width="560" height="315" src="https://www.youtube.com/embed/05rEefXlmhI" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+</html>
+<html>
+<iframe width="560" height="315" src="https://www.youtube.com/embed/uQj5UNhCPuo" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+</html>
+<br />
+<br />
+<html>
+<iframe width="560" height="315" src="https://www.youtube.com/embed/9zfeTw-uFCw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+</html>
+== Helpful resources ==
+* [https://www.analyticsvidhya.com/blog/2017/07/introduction-to-genetic-algorithm/ This article is a good place to get started]
+* a good game: https://andymakes.itch.io/combat-genetics
+* a better game to help you understand genetic algorithms: https://david-birge-cotte.itch.io/evolution-sandbox
+== Terms associated with genetic algorithms ==
+* [[Brute force approach]]
+* [[Computational intractability]]
+* [[Convergence]]
+* [[Crossover / crossover operator]]
+* [[Elitism]]
+* [[Fitness / fitness function / fitness landscape]]
+* [[Heuristics]]
+* [[Optimization]]
+* [[Population]]
+* [[Premature convergence]]
+* [[Problem space]]
+* [[Roulette wheel selection]]
+* [[Selection strategy]]
+* [[Simulated annealing]]
+* [[Termination condition]]
 == References ==