genetic-algorithm

A genetic algorithm implementation for Rust. Inspired by the book Genetic Algorithms in Elixir

There are three main elements to this approach:

• The Genotype (the search space)
• The Fitness function (the search goal)
• The strategy (the search strategy)
  • Evolve (evolution strategy)
  • Permutate (for small search spaces, with a 100% guarantee)
  • HillClimb (when search space is convex with little local optima or when crossover is impossible/inefficient)

Terminology:

• Population: a population has population_size number of individuals (called chromosomes).
• Chromosome: a chromosome has genes_size number of genes
• Gene: a gene is a combination of position in the chromosome and value of the gene (allele)
• Allele: alleles are the possible values of the genes
• Genotype: holds the genes_size and alleles and knows how to generate and mutate chromosomes efficiently
• Fitness: knows how to determine the fitness of a chromosome

Documentation

See docs.rs

Quick Usage

use genetic_algorithm::strategy::evolve::prelude::*;

// the search space
let genotype = BinaryGenotype::builder() // boolean alleles
    .with_genes_size(100)                // 100 genes per chromosome
    .build()
    .unwrap();

println!("{}", genotype);

// the search goal to optimize towards (maximize or minimize)
#[derive(Clone, Debug)]
pub struct CountTrue;
impl Fitness for CountTrue {
    type Genotype = BinaryGenotype;
    fn calculate_for_chromosome(&mut self, chromosome: &Chromosome<Self::Genotype>) -> Option<FitnessValue> {
        Some(chromosome.genes.iter().filter(|&value| *value).count() as FitnessValue)
    }
}

// the search strategy
let mut rng = rand::thread_rng();     // a randomness provider implementing Trait rand::Rng
let evolve = Evolve::builder()
    .with_genotype(genotype)
    .with_population_size(100)        // evolve with 100 chromosomes
    .with_target_fitness_score(100)   // goal is 100 times true in the best chromosome
    .with_fitness(CountTrue)          // count the number of true values in the chromosomes
    .with_crossover(CrossoverUniform::new(true)) // crossover all individual genes between 2 chromosomes for offspring
    .with_mutate(MutateOnce::new(0.2))     // mutate a single gene with a 20% probability per chromosome
    .with_compete(CompeteElite::new())       // sort the chromosomes by fitness to determine crossover order
    .with_extension(ExtensionNoop::new())    // extension step, disabled
    .call(&mut rng);
    .unwrap()

println!("{}", evolve);

Examples

Run with cargo run --example [EXAMPLE_BASENAME] --release

• N-Queens puzzle https://en.wikipedia.org/wiki/Eight_queens_puzzle.
  • See examples/evolve_nqueens
  • See examples/hill_climb_nqueens
  • UniqueGenotype<u8> with a 64x64 chess board setup
  • custom NQueensFitness fitness
• Knapsack problem: https://en.wikipedia.org/wiki/Knapsack_problem
  • See examples/evolve_knapsack
  • See examples/permutate_knapsack
  • BinaryGenotype<(weight, value)> each gene encodes presence in the knapsack
  • custom KnapsackFitness(&items, weight_limit) fitness
• Infinite Monkey theorem: https://en.wikipedia.org/wiki/Infinite_monkey_theorem
  • See examples/evolve_monkeys
  • DiscreteGenotype<char> 100 monkeys randomly typing characters in a loop
  • custom fitness using hamming distance
• Custom Fitness function with LRU cache
  • See examples/evolve_binary_lru_cache_fitness
  • Note: doesn't help performance much in this case...
• Permutation strategy instead of Evolve strategy for small search spaces, with a 100% guarantee
  • See examples/permutate_knapsack
• HillClimb strategy instead of Evolve strategy, when crossover is impossible or inefficient
  • See examples/hill_climb_nqueens
  • See examples/hill_climb_table_seating

Tests

Run tests with cargo test

Benchmarks

Implemented using criterion. Run benchmarks with cargo bench

Profiling

Implemented using criterion and pprof.

Find the flamegraph in: ./target/criterion/profile_evolve_binary/profile/flamegraph.svg

Run with cargo run --example profile_evolve_binary --release -- --bench --profile-time 5

TODO

• Make duration stats return Duration, so we can choose sec/milli/micro afterwards.
• Make fitness/simple_sum generic
• Does Fitness need an associated trait for Genotype? Can this be made more lightweight?
• Add simulated annealing strategy

MAYBE

• Add Roulette competition with and without duplicates (with fitness ordering)
• Add OrderOne crossover for UniqueGenotype?
• Add WholeArithmetic crossover for ContinuousGenotype?
• Rename Continuous to Real?

ISSUES

• MutateDynamicOnce and MutateDynamicRounds are mutable, but the MutateDispatch reinitializes each round. Fix in feature/enum_plugins branch