Genetic algorithm evolution

Two fully parallel genetic algorithm implementations in Rust, with zero external dependencies.

Comes with two genetic evolution drivers: continuous::ContinuousTrainer and stochastic::StochasticTrainer

• stochastic::StochasticTrainer trains using the typical discrete generational strategy: a random population of genomes is seeded to begin the process. They are individually evaluated for fitness, and then based on a given selection strategy, are either retained for the next generation or culled. The remaining genes reproduce using either crossover or minor mutations to determine the population of the next generation.
• continuous::ContinuousTrainer works using a more novel strategy: evolution occurs not in discrete steps, but instead continuously, constantly, without stopping. All genes are ranked from least to most fit in the population. At all times, random genes from the population weighted by their fitness are selected and chosen to be mutated / crossbred, modified, and re-inserted back into the population in their appropriate ranking based on their new fitness level. This continues constantly without stopping, until a set number of new children have been repopulated.

Using the trainers is simple. First, implement the Genome trait with your custom type. Then, construct a new trainer, and finally run its train method with the appropriate parameters. Training will begin automatically, and end once the ending criteria have been met. The trainers will report statistics about the population to the console on a periodic basis. Ending criteria, as well as reporting frequency, conditions, and methods, can all be configured in more detail using the train_custom method available on each trainer.

See tests/ for an example of how to use the trainers in this library.