Metaheuristics aim to generate or select a heuristic for an optimization problem, particularly where the set of sample solutions is too large to completely sample.
A handful of metaheuristic crates exists in the Rust ecosystem, most notably several libraries that make it straight-forward to write a genetic or evolutionary algorithms.
A library providing genetic algorithm execution.
Evolutionary algorithms library written in Rust.
Find approximate solutions to your optimisation problem using metaheuristics algorithms
EANT2 is an metaheuristicsary algorithm that uses the Common Genetic Encoding (CGE) to encode neural networks and CMA-ES as a weight optimizer. EANT2 evolves the topology of the Neural Network and CMA-ES tries to find the optimal weights for each topology.
Evolutionary Algorithm Library for Rust
a little lib to use genetic algorithm