Genetic algorithm
Genetic algorithms from selection, crossover, and mutation to an animated fitness demo that evolves a binary string toward more 1s.
Genetic algorithms (GAs) search large solution spaces by maintaining a population of candidates and iteratively applying selection, crossover, and mutation — operators inspired by natural evolution. John Holland formalized the approach in Adaptation in Natural and Artificial Systems (1975); today GAs show up in scheduling, design optimization, and other combinatorial problems where gradients are unavailable.
What the demo shows
The AlgoScape visualization evolves a population of binary strings. Fitness is the count of 1 bits, so you can see fitter individuals reproduce, crossover mix genes, and mutation keep diversity from collapsing. It is a toy objective, but the loop matches production GAs: evaluate → select → recombine → mutate → repeat.
Try it
Run the genetic algorithm visualization and watch fitness climb across generations.