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