Crate neat

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A simple crate that implements the Neuroevolution Augmenting Topologies algorithm using genetic-rs

You can get started by looking at genetic-rs docs and checking the examples for this crate.

Re-exports§

builtin: Built-in nextgen functions and traits to go with them.
prelude: Used to quickly import everything this crate has to offer. Simply add use genetic_rs::prelude::* to begin using this crate.
rand: Utilities for random number generation
runnable: A module containing the main NeuralNetwork struct. This has state/cache and will run the predictions. Make sure to run NeuralNetwork::flush_state between uses of NeuralNetwork::predict.
topology: A module containing the NeuralNetworkTopology struct. This is what you want to use in the DNA of your agent, as it is the thing that goes through nextgens and suppors mutation.

CrossoverReproduction: Used in crossover-reproducing next_gens
DivisionReproduction: Used in dividually-reproducing next_gens
FitnessFn: Represents a fitness function. Inputs a reference to the genome and outputs an f32.
GenerateRandom: Helper trait used in the generation of random starting populations
GenerateRandomCollection: Blanket trait used on collections that contain objects implementing GenerateRandom
NextgenFn: Represents a nextgen function. Inputs genomes and rewards and produces the next generation
Prunable: Used in pruning next_gens
RandomlyMutable: Used in all of the builtin next_gens to randomly mutate genomes a given amount

crossover_pruning_nextgen: Prunes half of the genomes and randomly crosses over the remaining ones.
division_pruning_nextgen: When making a new generation, it despawns half of the genomes and then spawns children from the remaining to reproduce.
scrambling_nextgen: When making a new generation, it mutates each genome a certain amount depending on their reward. This nextgen is very situational and should not be your first choice.