Crate rosomaxa

This crate exposes a generalized hyper heuristics and some helper functionality which can be used to build a solver for optimization problems.

Examples

This example demonstrates the usage of example models and heuristics to minimize Rosenbrock function. For the sake of minimalism, there is a pre-built solver and heuristic operator models. Check example module to see how to use functionality of the crate for an arbitrary domain.

use rosomaxa::prelude::*;
use rosomaxa::example::*;

let random = Arc::new(DefaultRandom::default());
// examples of heuristic operator, they are domain specific. Essentially, heuristic operator
// is responsible to produce a new, potentially better solution from the given one.
let noise_op = VectorHeuristicOperatorMode::JustNoise(Noise::new_with_ratio(1., (-0.1, 0.1), random));
let delta_op = VectorHeuristicOperatorMode::JustDelta(-0.1..0.1);
let delta_power_op = VectorHeuristicOperatorMode::JustDelta(-0.5..0.5);

// add some configuration and run the solver
let (solutions, _) = Solver::default()
    .with_fitness_fn(create_rosenbrock_function())
    .with_init_solutions(vec![vec![2., 2.]])
    .with_search_operator(noise_op, "noise", 1.)
    .with_search_operator(delta_op, "delta", 0.2)
    .with_diversify_operator(delta_power_op)
    .with_termination(Some(5), Some(1000), None, None)
    .solve()
    .expect("cannot build and use solver");

// expecting at least one solution with fitness close to 0
assert_eq!(solutions.len(), 1);
let (_, fitness) = solutions.first().unwrap();
assert!(*fitness < 0.001);

Modules

algorithms

A collection of reusable algorithms without dependencies on any other module in the project.

evolution

Contains functionality to run evolution simulation.

example

This module contains example models and logic to demonstrate practical usage of rosomaxa crate.

hyper

This module contains a hyper-heuristic logic.

population

Specifies population types.

prelude

This module reimports a commonly used types.

termination

The termination module contains logic which defines termination criteria for metaheuristic, e.g. when to stop evolution in evolutionary algorithms.

utils

This module contains helper functionality.

Structs

HeuristicStatistics

A refinement statistics to track evolution progress.

TelemetryHeuristicContext

A default heuristic context implementation which uses telemetry to track search progression parameters.

Enums

HeuristicSpeed

Defines instant refinement speed type.

Traits

HeuristicContext

Represents heuristic context.

HeuristicSolution

Represents solution in population defined as actual solution.

Stateful

A trait which specifies object with state behavior.

Functions

get_default_population

Gets default population algorithm.

get_default_selection_size

Gets default population selection size.

Type Aliases

DynHeuristicPopulation

Specifies a dynamically dispatched type for heuristic population.

HeuristicResult

Specifies a heuristic result type.