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 genetic or evolutionary algorithms.
Mathematical optimization in pure Rust
A library providing genetic algorithm execution.
Evolutionary algorithms library written in Rust.
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.
Rust implementation of real-coded genetic algorithm for solving optimization problems and training of neural networks. The latter is also known as neuroevolution.
Evolutionary Algorithm Library for Rust
A Rust implementation of the Simple(x) black-box optimization algorithm.
Find approximate solutions to your optimisation problem using metaheuristics algorithms
a little lib to use genetic algorithm