Evolving JavaScript: Cultivating Genetic Algorithms for Creative Coding
summary
JavaScript code on canvas implements genetic algorithms with roulette-wheel selection, crossover and per-gene mutation, and the repo and demo are on GitHub. A simple circle population found a match in Generation 7 by measuring position and size fitness; a particle swarm with ray-based sensing climbs from ~5% success to particles reach 100% by next generation after tuning population size, fitness weighting and mutation rate. Mentions p5js, requestAnimationFrame, and concrete generation counts and convergence behavior.
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chapters (11)
- 0:00Introduction
- 2:33JavaScript evolution
- 3:36Genetic algorithms history
- 6:49Purpose of genetic algorithms
- 9:55How genetic algorithms work
- 14:08Creative coding with genetic algorithms
- 18:43Particle simulation demo
- 22:20Canvas working tips
- 24:24Genetic algorithm parameters
- 25:51Resources for genetic algorithms
- 28:42Closing thoughts
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