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
• Allele: alleles are the possible values of the genes
• Gene: a gene is a combination of position in the chromosome and value of the gene (allele)
• Genes: storage trait of the genes for a chromosome, mostly Vec<Allele>, but alternatives possible
• Genotype: Knows how to generate, mutate and crossover chromosomes efficiently
• Fitness: knows how to determine the fitness of a chromosome

All multithreading mechanisms are implemented using rayon::iter and std::sync::mpsc.

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; // Genes = Vec<bool>
    fn calculate_for_chromosome(
        &mut self, 
        chromosome: &FitnessChromosome<Self>, 
        _genotype: &FitnessGenotype<Self>
    ) -> Option<FitnessValue> {
        Some(chromosome.genes.iter().filter(|&value| *value).count() as FitnessValue)
    }
}

// the search strategy
let evolve = Evolve::builder()
    .with_genotype(genotype)
    .with_select(SelectElite::new(0.9))               // sort the chromosomes by fitness to determine crossover order and select 90% of the population for crossover (drop 10% of population)
    .with_crossover(CrossoverUniform::new())          // crossover all individual genes between 2 chromosomes for offspring (and restore back to 100% of target population size by keeping the best parents alive)
    .with_mutate(MutateSingleGene::new(0.2))          // mutate offspring for a single gene with a 20% probability per chromosome
    .with_fitness(CountTrue)                          // count the number of true values in the chromosomes
    .with_fitness_ordering(FitnessOrdering::Maximize) // optional, default is Maximize, aim towards the most true values
    .with_target_population_size(100)                 // evolve with 100 chromosomes
    .with_target_fitness_score(100)                   // goal is 100 times true in the best chromosome
    .with_reporter(EvolveReporterSimple::new(100))    // optional builder step, report every 100 generations
    .call();
    .unwrap()

println!("{}", evolve);

// it's all about the best genes after all
let (best_genes, best_fitness_score) = evolve.best_genes_and_fitness_score().unwrap();
assert_eq!(best_genes, vec![true; 100]);
assert_eq!(best_fitness_score, 100);

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<Item(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
  • ListGenotype<char> 100 monkeys randomly typing characters in a loop
  • custom fitness using hamming distance
• 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
• Explore vector genes (BinaryGenotype) versus other storage (BitGenotype)
  • See examples/evolve_bit_v_binary
• Explore internal and external multithreading options
  • See examples/explore_multithreading
• Use superset StrategyBuilder for easier switching in implementation
  • See examples/explore_strategies
• Use fitness LRU cache
  • See examples/evolve_binary_cache_fitness
  • Note: doesn't help performance much in this case... or any case, better fix your population diversity
• Custom Reporting implementation
  • See examples/permutate_scrabble

Performance considerations

For the Evolve strategy:

• Reporting: start with EvolveReporterSimple for basic understanding of:
  • fitness v. framework overhead
  • staleness and population characteristics (cardinality etc.)
• Select: no considerations. All selects are basically some form of in-place sorting of some kind. This is relatively fast compared to the rest of the operations.
• Crossover: the workhorse of internal parts. Crossover touches most genes each generation and clones up to the whole population to restore lost population size in selection. See performance tips below. It also calculates new genes hashes if enabled on the Genotype, which has a relatively high overhead on the main Evolve loop.
• Mutate: no considerations. It touches genes like crossover does, but should be used sparingly anyway; with low gene counts (<10%) and low probability (5-20%)
• Fitness: can be anything. This fully depends on the user domain. Parallelize it using with_par_fitness() in the Builder. But beware that parallelization has it's own overhead and is not always faster.

Performance Tips

• Small genes sizes
  • It seems that CrossoverMultiGene with number_of_crossovers = genes_size / 2 and allow_duplicates = true is the best tradeoff between performance and effect. CrossoverUniform is an alias for the same approach, taking the genes_size from the genotype at runtime.
  • Restoring the population doesn't matter that much as the cloning is relatively less pronounced (but becomes more prominent for larger population sizes)
• Large genes sizes
  • It seems that CrossoverMultiPoint with number_of_crossovers = genes_size / 9 and allow_duplicates = false is the best tradeoff between performance and effect.
  • Restoring the population has considerable performance effects. Use a high selection_rate or even 100%, so there is little parent cloning. Explore non-Vec based genotypes like BitGenotype.

GPU acceleration

There are two genotypes where Genes (N) and Population (M) are a stored in single contiguous memory range of Alleles (T) with length N*M on the heap. A pointer to this data can be taken to calculate the whole population at once. These are:

• DynamicMatrixGenotype
• StaticMatrixGenotype

Useful in the following strategies where a whole population is calculated:

• Evolve
• HillClimb-SteepestAscent

Possibly a GPU compatible memory layout still needs to be added. The current implementation just provides all the basic building blocks to implement this. Please open a github issue for further support.

Tests

Run tests with cargo test

Use .with_rng_seed_from_u64(0) builder step to create deterministic tests results.

Benchmarks

Implemented using criterion. Run benchmarks with cargo bench

Profiling

Implemented using criterion and pprof.

Uncomment in Cargo.toml

[profile.release]
debug = 1

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

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

TODO

MAYBE

• Target cardinality range for Mutate Dynamic to avoid constant switching (noisy in reporting events)
• Add scaling permutate? Can be done by grid search and then search within last grid with new scale
• Add chromosome_permutations_size for all genotypes, it's just useful informatively, maybe make option for RangeGenotype?
• Add scaling helper function
• Add simulated annealing strategy
• Add Roulette selection with and without duplicates (with fitness ordering)
• Add OrderOne crossover for UniqueGenotype?
• Add WholeArithmetic crossover for RangeGenotype?
• Add CountTrueWithWork instead of CountTrueWithSleep for better benchmarks?
• Explore more non-Vec genes: PackedSimd?
• Maybe use TinyVec for Population? (it us usually less than 1000 anyway), maybe useful paired with MatrixGenotype, where the chromosomes are lightweight (and Copyable)
• StrategyBuilder, with_par_fitness_threshold, with_permutate_threshold?
• Add target fitness score to Permutate? Seems illogical, but would be symmetrical. Don't know yet

ISSUES

• hill_climb SteepestAscent actually has a population size requirement of neighbouring_population_size + 1, because of the working chromosome. This could overflow StaticMatrixGenotype<T, N, M>, use StaticMatrixGenotype<T, N, { M + 1 }> as workaround