I wrote a traveling salesman problem in rust and build it with wasm bindgen.

1. rayon::join doesn't work on wasm32, so I limit the population under 50.
2. time::get_time is unimplemented!. so I stub the time crate return value with 0.

demo https://warycat.github.io/ga_wasm/ src https://github.com/warycat/ga_wasm

Thank you for making this awesome crate.

I'm currently implementing a genetic algorithm that runs a circuit simulation to evaluate a circuits fitness. To perform the simulation, I use a third party C API. This API is called from within the fitness_of() function of the FitnessFunction trait I defined.

The third party C API I use cannot be used in a multi-threaded fashion. so I'm wondering if it's possible to disable the parallelism in the ga module; or if there is a less performant, sequential version of genevo.

Default mutation operators for basic encoding types are helpful to use the library for simulations with basic genotypes.

With this issue mutation operators for the following 3 encoding types will be supported:

• binary encoding
• value encoding
• permutation encoding

Default crossover operators for basic encoding types are helpful to use the library for simulations with basic genotypes.

With this issue crossover operators for the following 3 encoding types will be supported:

• binary encoding
• value encoding
• permutation encoding

Without knowing anything about the encoding of gene it is impossible to provide default implementations for crossover and mutation operator. Therefore it would make sense to implement the basic types of gene encodings, which are:

• binary encoding
• value encoding
• permutation encoding
• tree encoding

the implementation will be split up into 2 parts. with this feature request only the first 3 encoding types:

• binary encoding
• value encoding
• permutation encoding

will be implemented. For tree encoding a separate issue will be created.

A crater run found that this crate's tests no longer compile with rust 1.60 beta (see https://github.com/rust-lang/rust/issues/94504) due to a newly added method causing conflicts in method resolution.

The regression has been deemed an acceptable minor change per RFC 1105. It can be fixed by using fully qualified function call syntax to unambiguously call the extension trait that previously provided abs_diff or by switching to the new std API once 1.60 is released.

I received an index out of bounds error when using an OrderOneCrossover operator: index out of bounds: the len is 2 but the index is 2. I believe, this is due to the second index p2_index in the cyclic crossover function being compared to the wrong vector's size.

I received an index out of bounds error when using an OrderOneCrossover operator: index out of bounds: the len is 2 but the index is 2. I believe, this is due to the second index p2_index in the cyclic crossover function being compared to the wrong vector's size.

this PR is for the case when using Vec<bool> as the Genotype in combination with RandomValueMutator.

As a result, we can use the built-in Vec<bool> instead of the fixedbitset crate for binary encoded individual.

sample codes:

    let algorithm = genetic_algorithm()
        .with_evaluation(MagFitnessEvaluator)
        .with_selection(RouletteWheelSelector::new(0.7, 2))
        .with_crossover(MultiPointCrossBreeder::new(3))
        .with_mutation(RandomValueMutator::new(0.1, false, true))
        .with_reinsertion(ElitistReinserter::new(MagFitnessEvaluator, false, 0.7))
        .with_initial_population(initial_population)
        .build();

Since execution of crossover and mutation is done in parallel, the processing time is no longer correct. The processing time should be meassured in each thread separately.

innoave/genevo now has a Chat Room on Gitter

@haraldmaida has just created a chat room. You can visit it here: https://gitter.im/innoave/genevo.

This pull-request adds this badge to your README.md:

If my aim is a little off, please let me know.

Happy chatting.

PS: Click here if you would prefer not to receive automatic pull-requests from Gitter in future.

I'm no biologist, but reading definitions, should structs/traits/fns with GenomeBuilder be rather called GenotypeBuilder? Also Evaluated::genome should be a genotype?

Quick question, is it possible to get the current population after calling Simulator::step()?

It seems step() returns an EvaluatedPopulation, which you can query using EvaluatedPopulation::individuals(), but this is the population before applying selection. I want to get the population after applying selection (so after selecting the best parents and cross-breeding them). Is this possible at all?

(Basically I'm looking for a way to access GeneticAlgorithm.population directly, which is not possible at the moment since it's private)

Hello,

I am currently stuck using a janky float discretization technique because I can't use floats directly.

Is there a better workaround? Is there some truly compelling reason we cannot implement the Fitness trait for floats internally like is done for the signed and unsigned integers?

Thank you.

Hello! I'm working under a handler that will help us fix the genotype after mutation, if necessary. I’m very interested in your opinion about this, maybe I'm doing something wrong.

Hi, I'm doing my first experiments in the field of GAs. After looking at all crates matching "genetic", genevo looked to me as the most promising library. Great work! For others doing the same evaluation, a list of features in the README would help a lot. And for a beginner like me, more documentation would be immensely helpful. Ideal would be an exerpt of OBI98 with included code examples. Maybe you would get the permission for copying parts of it (see FAQ)? But anyway, this issue is mostly to thank you for your work! Gruss, Pirmin

Hi, I've changed OrderOneCrossover and PartiallyMappedCossover to a trait based implemenation, because I wanted to have my permutation encoding in something different than Vec<usize>. Since I'm an absolute beginner in the field of GAs, I don't know whether my approach makes sense though. Thanks for your great work! Pirmin