A genetic programming system that uses Elixir AST as a program representation and can evolve native Elixir language solutions to problems.
This is currently at a proof-of-concept level of maturity.
There are a couple of simple sample problems included in this repo, in the
samples subdirectory. The problems here are "symbolic regression" curve fitting
problems, in which the GP system attempts to evolve code which maps a series of
numerical inputs to the provided outputs.
These samples are "toy" problems - they are intended to be solvable but slightly challenging in order to test the GP system and demonstrate how it works.
To run via the command-line, you can use the included escript:
mix escript.build
./quotegp ./samples/sample1.json
To run via code:
# Generate some test data:
test_cases = 8..16 |> Enum.map(fn x -> {x, x * x + 3 * x + 8} end)
# Configure and run:
config = %QuoteGP.Config{}
QuoteGP.Population.build(config)
|> QuoteGP.Population.run(test_cases)
For more advanced usage, configure a GP run with the following options (shown with defaults):
%QuoteGP.Config{
population_size: 1000,
max_generations: 100,
max_program_depth: 10,
mutation_probability: 0.1,
halt_fitness: 0.0,
mutation_rate: 0.3,
crossover_rate: 0.3,
tournament_size: 9
}
- halt_fitness: a fitness at which execution will halt
- max_generations: the maximum number of generations to run if a solution is not found
- population_size: the number of individuals in the evolving population
- max_program_depth: the maximum depth of a program in the population
- mutation_probability: 0.1,
- mutation_rate: 0.3,
- crossover_rate: 0.3,
- tournament_size: the number of individuals in a "tournament" selection event