Tight Genes: Intro to Genetic Algorithms
summary
Explains genetic algorithms by building a tiny Ruby evolver class that runs: initialize a population (10 candidates in demos), score fitness (truck knapsack yields best from 20,000 to 26,000 value), select breeders, crossover, mutate (1-in-4 bit flips for items, 1-in-3 +/-1 for D&D stats), and repeat until done; demonstrates Dungeons & Dragons stat evolution and mead recipes (7 to 149 generations to converge) and cites the real-world NASA ST5 antenna as a surprising successful use.
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chapters (9)
- 0:00Genetic algorithms intro
- 3:33History and applications
- 10:54How GA works step by step
- 16:18Initial population setup
- 23:25Evolving D&D fighter stats
- 32:53Extracting Evolver class
- 35:00Evolving mead recipes
- 39:59Recap and use cases
- 41:50Q&A
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