oxigen

Oxigen is a parallel genetic algorithm library implemented in Rust. The name comes from the merge of OXIdación (Rust translated to Spanish) and GENetic.

Oxigen provides the following features:

• Fast and parallel genetic algorithm implementation (it solves the N Queens problem for N=255 in few seconds). For benchmarks view benchmarks section of this file.
• Customizable mutation and selection rates with constant, linear and Quadratic functions according to generations built-in (you can implement your own functions via the MutationRate and SelectionRate traits).
• Customizable age unfitness of individuals, with no unfitness, linear and Quadratic unfitness with threshold according to generations of the individual built-in (you can implement your own age functions via the Age trait).
• Accumulated Roulette, Tournaments and Cup built-in selection functions (you can implement your own selection functions via the Selection trait).
• SingleCrossPoint, MultiCrossPoint and UniformCross built-in crossover functions (you can implement your own crossover function via the Crossover trait).
• Many built-in survival pressure functions. You can implement your own survival pressure functions via the SurvivalPressure trait.
• Niches built-in PopulationRefitness function. You can implement your own population refitness functions via the PopulationRefitness trait.
• SolutionFound, Generation and Progress and more built-in stop criteria (you can implement your own stop criteria via the StopCriterion trait).
• Genotype trait to define the genotype of your genetic algorithm. Whatever struct can implement the Genotype trait under the following restrictions:
  • It has a iter function that returns a use std::slice::Iter over its genes.
  • It has a into_iter function that consumes the individual and returns a use std::vec::IntoIter over its genes.
  • It has a from_iter function that set the genes from an iterator.
  • It implements Display, Clone, Send and Sync.
  • It has functions to generate a random individual, to mutate an individual, to get the fitness of an individual and to know if and individual is_solution of the problem.
• Individual's fitness is cached to not do unnecessary recomputations (this can be disabled with .cache_fitness(false) if your fitness function is stochastic and so you need to recompute fitness in each generation).
• Progress statistics can be configured to be printed every certain number of generations to a file.
• Population individuals with their fitnesses can be configured to be printed every certain number of generations to a file.
• Specific initial individuals can be inserted in the genetic algorithm execution.
• Genetic executions can be resumed using the population of the last generation as initial population.
• Coevolution is possible executing little genetic algorithm re-executions inside the fitness function.

Differences between 2 and 1 versions:

• Oxigen 2 is more flexible because any struct with a Vec inside can implement Genotype. In 1 versions this was not possible because Genotype had to implement FromIterator. In 2 versions a from_iter function has been added instead.
• Oxigen 2 fix the issue #3 ('Cuadratic' has been replaced by 'Quadratic' in built-in enums). This has not been fixed in 1 versions to not break the interface.
• The fix function in Genotype returns a boolean to specify if the individual has been changed to recompute its fitness.
• The number of solutions gotten in each generation is now the number of different solutions using the new distance function of Genotype.
• The u16 type has been changed by usize in StopCriterion, MutationRate and SelectionRate traits.
• PopulationRefitness trait has been added to optionally refit the individuals of the population comparing them to the other individuals. Niches built-in PopulationRefitness function has been added.
• The SurvivalPressure trait has been redefined and now it kills the individuals instead of returning the indexes to remove. It also receives a list with the pairs of parents and children of the generation.
• Many SurvivalPressure built-in functions have been added, like Overpouplation, CompetitiveOverpopulation, DeterministicOverpopulation, ChildrenFightParents, ChildrenFightMostsimilar, etc.
• The two previous additions allow to search different solutions in different search space areas in order to avoid local suboptimal solutions and find different solutions.
• Other minor improvements.

New in version 2.1:

The optional feature global_cache adds a HashMap saving the evaluation of each individual in the full execution.

This cache is useful when the evaluation of each individual is expensive, and it complements the individual-based cache already existing in previous versions (if an individual has been evaluated it is not reevaluated unless cache_fitness is false). In other words, this global cache saves the evaluation of new individuals that are equal to another individual that was evaluated before.

Note that the global cache is not always better, since if the fitness function is cheap the cost of getting and inserting into the cache can be more expensive than it. Take also into account the increasing of RAM usage of the global cache.

To enable the global cache add the feature global_cache in the Cargo.toml of your project and set to true the cache_fitness (always true by default) and global_cache (true by default when the global_cache is enabled) properties of your GeneticExeution. Example of Cargo.toml:

[dependencies]
oxigen = { version="^2.1", features=["global_cache"] }

Usage

In your Cargo.toml file add the oxigen dependency:

[dependencies]
oxigen = "^2"

To use oxigen use oxigen::prelude::* and call the run method over a GeneticExecution instance overwriting the default hyperparameters and functions folllowing your needs:

let n_queens: u8 = std::env::args()
    .nth(1)
    .expect("Enter a number between 4 and 255 as argument")
    .parse()
    .expect("Enter a number between 4 and 255 as argument");

let progress_log = File::create("progress.csv").expect("Error creating progress log file");
let population_log = File::create("population.txt").expect("Error creating population log file");
let log2 = (f64::from(n_queens) * 4_f64).log2().ceil();

let population_size = 2_i32.pow(log2 as u32) as usize;

let (solutions, generation, progress) = GeneticExecution::<u8, QueensBoard>::new()
    .population_size(population_size)
    .genotype_size(n_queens as u8)
    .mutation_rate(Box::new(MutationRates::Linear(SlopeParams {
        start: f64::from(n_queens) / (8_f64 + 2_f64 * log2) / 100_f64,
        bound: 0.005,
        coefficient: -0.0002,
    })))
    .selection_rate(Box::new(SelectionRates::Linear(SlopeParams {
        start: log2 - 2_f64,
        bound: log2 / 1.5,
        coefficient: -0.0005,
    })))
    .select_function(Box::new(SelectionFunctions::Cup))
    .age_function(Box::new(AgeFunctions::Quadratic(
        AgeThreshold(50),
        AgeSlope(1_f64),
    )))
    .progress_log(20, progress_log)
    .population_log(2000, population_log)
    .run();

For a full example visit the nqueens-oxigen example.

For more information visit the documentation.

Resuming a previous execution

Since version 1.1.0, genetic algorithm executions return the population of the last generation and new genetic executions accept a initial population. This permits to resuming previous executions and it also enables coevolution, since little genetic algorithm re-executions can be launched in the fitness function.

In the following example a execution with 10000 generations is launched and after it is resumed until finding a solution with different rates.

let n_queens: u8 = std::env::args()
    .nth(1)
    .expect("Enter a number between 4 and 255 as argument")
    .parse()
    .expect("Enter a number between 4 and 255 as argument");

let progress_log = File::create("progress.csv").expect("Error creating progress log file");
let population_log = File::create("population.txt").expect("Error creating population log file");
let log2 = (f64::from(n_queens) * 4_f64).log2().ceil();

let population_size = 2_i32.pow(log2 as u32) as usize;

let (_solutions, _generation, _progress, population) = GeneticExecution::<u8, QueensBoard>::new()
    .population_size(population_size)
    .genotype_size(n_queens as u8)
    .mutation_rate(Box::new(MutationRates::Linear(SlopeParams {
        start: f64::from(n_queens) / (8_f64 + 2_f64 * log2) / 100_f64,
        bound: 0.005,
        coefficient: -0.0002,
    })))
    .selection_rate(Box::new(SelectionRates::Linear(SlopeParams {
        start: log2 - 2_f64,
        bound: log2 / 1.5,
        coefficient: -0.0005,
    })))
    .select_function(Box::new(SelectionFunctions::Cup))
    .age_function(Box::new(AgeFunctions::Quadratic(
        AgeThreshold(50),
        AgeSlope(1_f64),
    )))
    .stop_criterion(Box::new(StopCriteria::Generation(10000)))
    .run();

let (solutions, generation, progress, _population) = GeneticExecution::<u8, QueensBoard>::new()
    .population_size(population_size)
    .genotype_size(n_queens as u8)
    .mutation_rate(Box::new(MutationRates::Linear(SlopeParams {
        start: f64::from(n_queens) / (8_f64 + 4_f64 * log2) / 100_f64,
        bound: 0.005,
        coefficient: -0.0002,
    })))
    .selection_rate(Box::new(SelectionRates::Linear(SlopeParams {
        start: log2 - 4_f64,
        bound: log2 / 1.5,
        coefficient: -0.0005,
    })))
    .select_function(Box::new(SelectionFunctions::Cup))
    .age_function(Box::new(AgeFunctions::Quadratic(
        AgeThreshold(50),
        AgeSlope(1_f64),
    )))
    .population(population)
    .progress_log(20, progress_log)
    .population_log(2000, population_log)
    .run();

Building

To build oxigen, use cargo like for any Rust project:

• cargo build to build in debug mode.
• cargo build --release to build with optimizations.

To run benchmarks, you will need a nightly Rust compiler. Uncomment the lines // #![feature(test)] and // mod benchmarks; from lib.rs and then bechmarks can be run using cargo bench.

Benchmarks

The following benchmarks have been created to measure the genetic algorithm functions performance:

running 25 tests
test benchmarks::bench_cross_multi_point_255inds                                                           ... bench:     348,371 ns/iter (+/- 10,506)
test benchmarks::bench_cross_single_point_255inds                                                          ... bench:     113,986 ns/iter (+/- 10,657)
test benchmarks::bench_cross_uniform_255inds                                                               ... bench:      88,426 ns/iter (+/- 2,302)
test benchmarks::bench_distance_255                                                                        ... bench:      20,715 ns/iter (+/- 1,648)
test benchmarks::bench_fitness_1024inds                                                                    ... bench:     377,344 ns/iter (+/- 8,617)
test benchmarks::bench_fitness_age_1024inds                                                                ... bench:      31,360 ns/iter (+/- 1,204)
test benchmarks::bench_fitness_age_not_cached_1024inds                                                     ... bench:     395,056 ns/iter (+/- 24,407)
test benchmarks::bench_fitness_global_cache_1024inds                                                       ... bench:     340,087 ns/iter (+/- 28,889)
test benchmarks::bench_fitness_not_cached_1024inds                                                         ... bench:     373,966 ns/iter (+/- 60,244)
test benchmarks::bench_get_fitnesses_1024inds                                                              ... bench:      18,951 ns/iter (+/- 868)
test benchmarks::bench_get_solutions_1024inds                                                              ... bench:      30,133 ns/iter (+/- 1,612)
test benchmarks::bench_mutation_1024inds                                                                   ... bench:          13 ns/iter (+/- 0)
test benchmarks::bench_selection_cup_255inds                                                               ... bench:     344,873 ns/iter (+/- 40,519)
test benchmarks::bench_selection_roulette_256inds                                                          ... bench:     140,994 ns/iter (+/- 1,294)
test benchmarks::bench_selection_tournaments_256inds                                                       ... bench:     420,272 ns/iter (+/- 49,178)
test benchmarks::bench_survival_pressure_children_fight_most_similar_255inds                               ... bench:  14,948,961 ns/iter (+/- 989,864)
test benchmarks::bench_survival_pressure_children_fight_parents_255inds                                    ... bench:     108,691 ns/iter (+/- 157)
test benchmarks::bench_survival_pressure_children_replace_most_similar_255inds                             ... bench:  14,935,080 ns/iter (+/- 1,221,611)
test benchmarks::bench_survival_pressure_children_replace_parents_255inds                                  ... bench:     167,392 ns/iter (+/- 15,839)
test benchmarks::bench_survival_pressure_children_replace_parents_and_the_rest_most_similar_255inds        ... bench: 737,347,573 ns/iter (+/- 16,773,890)
test benchmarks::bench_survival_pressure_children_replace_parents_and_the_rest_random_most_similar_255inds ... bench:   7,757,258 ns/iter (+/- 612,047)
test benchmarks::bench_survival_pressure_competitive_overpopulation_255inds                                ... bench:  10,727,219 ns/iter (+/- 770,681)
test benchmarks::bench_survival_pressure_deterministic_overpopulation_255inds                              ... bench:     173,745 ns/iter (+/- 609)
test benchmarks::bench_survival_pressure_overpopulation_255inds                                            ... bench:  10,710,336 ns/iter (+/- 657,369)
test benchmarks::bench_survival_pressure_worst_255inds                                                     ... bench:      20,318 ns/iter (+/- 298)
test benchmarks::bench_update_progress_1024inds                                                            ... bench:       7,424 ns/iter (+/- 273)

These benchmarks have been executed in a Intel Core i7 6700K with 16 GB of DDR4 and a 512 GB Samsung 950 Pro NVMe SSD in ext4 format in Fedora 30 with Linux kernel 5.2.9.

The difference of performance among the different fitness benchmarks have the following explanations:

• bench_fitness measures the performance of a cached execution cleaning the fitnesses after each bench iteration. This cleaning is the reason of being a bit slower than not cached benchmarks.
• bench_fitness_age measures the performance with fitness cached in all bench iterations, so it is very much faster.
• Not cached benchmarks measure the performance of not cached executions, with 1 generation individuals in the last case, so the performance is similar but a bit slower for the benchmark that must apply age unfitness.
• The children_fight_most_similar and children_replace_most_similar functions have to call the distance function c * p times, where c is the number of children and p is the size of the population (255 and 1024 respectively in the benchmarks).
• The overpopulation and competitive_overpopulation functions are similaer to children_replace_most_similar and children_fight_most_similar except to they are compared only with m individuals of the population (m is bigger than the number of children and smaller than the population size, 768 in the benchmarks). Therefore, 3/4 of the comparisons are done in these benchmarks compared to children_replace_most_similar and children_fight_most_similar.
• children_replace_parents_and_the_rest_random_most_similar is similar to children_replace_parents but, after it, random individuals are chosen to fight against the most similar individual in the population until the population size is the original population size. This means between 0 and 254 times random chosing and distance cmoputation over the entire population in function of the repeated parents in each generation.
• children_replace_parents_and_the_rest_most_similar is like the previous function but it searchs the pairs of most similar individuals in the population, which means p² distance function calls (2²⁰ in the benchmark).

Contributing

Contributions are absolutely, positively welcome and encouraged! Contributions come in many forms. You could:

1. Submit a feature request or bug report as an issue.
2. Ask for improved documentation as an issue.
3. Comment on issues that require feedback.
4. Contribute code via pull requests, don't forget to run cargo fmt before submitting your PR!

We aim to keep Rocket's code quality at the highest level. This means that any code you contribute must be:

• Commented: Public items must be commented.
• Documented: Exposed items must have rustdoc comments with examples, if applicable.
• Styled: Your code should be rustfmt'd when possible.
• Simple: Your code should accomplish its task as simply and idiomatically as possible.
• Tested: You should add (and pass) convincing tests for any functionality you add when it is possible.
• Focused: Your code should do what it's supposed to do and nothing more.

Note that unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in oxigen by you shall be dual licensed under the MIT License and Apache License, Version 2.0, without any additional terms or conditions.

Reference

Pozo, M.M. "Oxigen: Fast, parallel, extensible and adaptable genetic algorithm library written in Rust".

Bibtex

@misc{
  title={Oxigen: Fast, parallel, extensible and adaptable genetic algorithm library written in Rust},
  author={Pozo M.M.},
  howpublised = "\url{https://github.com/Martin1887/oxigen}"
}

License

oxigen is licensed under Mozilla Public License 2.0.