Genetic Algorithms: Difference between revisions

Revision as of 10:33, 9 December 2021

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

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

A video to get you started

Helpful resources

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

References

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

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

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+== 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