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
Fast, parallel, extensible and adaptable genetic algorithm library.
Flexible and modular single or multi-objective solver for contiguous and discrete problems
A Rust implementation of the Simple(x) black-box optimization algorithm.
A library providing genetic algorithm execution.
Find approximate solutions to your optimisation problem using metaheuristics algorithms
Evolutionary algorithms library written in Rust.
a little lib to use genetic algorithm
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
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.