Evolutionary

A fully extensible Rust framework for using paralyzed genetic algorithms to solve problems.

Currently, it supports coding in Binary, Real, Permuted Integers, Integers and any other coding you may want to implement. Check out the built-in implementation for the genetic operators:

• Selection
• Crossover
• Mutation

You can also code your own selection, crossover or mutation implementing the traits and passing them to the EvolutionBuilder.

Getting Started:

First you'll need to code your Fitness function:

use evolutionary::prelude::*;

#[derive(Clone)]
pub struct MaxFitness;

impl Fitness<Bin> for MaxFitness {
    fn calculate_fitness(&self, individual: &Bin) -> f64 {
        let mut sum = 0.;
  
        for i in 0..individual.get_chromosome().len() {
            if individual.get_gene(i) {
                sum += 1.;
            }
        }
  
        sum
    }
}

Then you will be able to build an evolution object using the EvolutionBuiler and setting all the required parameters:

fn main() {
    let mut evolution = EvolutionBuilder::new(30, 10, GeneCod::Bin, ())
        .with_fitness(MaxFitness)
        .with_selection(TournamentSelection::default())
        .with_crossover(NPointsCrossover::default())
        .with_mutation(BitSwapMutation::default())
        .with_stop_condition(move |_, iterations, _| iterations >= 1000)
        .build().unwrap();

    evolution.run();

    println!("Best individual: {:?}", evolution.current_best());
    println!("Best fitness: {}", evolution.current_best_fitness());
}

There is an extended getting started here.

Examples and Projects:

• evolutionary-examples - A project with some visual examples of the evolutionary library use, implemented with the bevy game engine.

There are some examples in the examples folder:

• Max
• Math Function
• NQueens
• Valued NQueens
• Maximize Radios Factory Profit
• Maze
• Subway

TODO:

• Individuals:
  • Tree-based chromosomes
• Selection:
  • Parallelize the SUS Selection
• Crossover:
  • Real:
    • Linear Crossover (LX)
    • Simulated Binary Crossover (SBX)
• Usability and Performance:
  • Logs System
  • Create macros to simplify the implementation of the traits where possible;
  • Allow fitness to be a function and not a struct that must be implemented
• Examples and Benchmark
  • Implement and Optimize the Salesman problem