Basic usage

Follow these steps to solve a basic regression problem:

1. Define an objective function. The objective function takes an individual as an argument and updates the fitness of the individual.

   def objective(individual):
       individual.fitness = ...
       return individual
   

2. Define parameters for the population, the genome, the evolutionary algorithm and the evolve function.

   population_params = {"n_parents": 10, "mutation_rate": 0.5, "seed": 8188211}
   
   genome_params = {
        "n_inputs": 2,
        "n_outputs": 1,
        "n_columns": 10,
        "n_rows": 2,
        "levels_back": 5,
        "primitives": (cgp.Add, cgp.Sub, cgp.Mul, cgp.Div, cgp.ConstantFloat),
        }
   
   ea_params = {"n_offsprings": 10, "tournament_size": 2, "n_processes": 2}
   
   evolve_params = {"max_generations": 1000, "min_fitness": 0.0}
   

3. Initialize a population and an evolutionary algorithm instance:

   pop = cgp.Population(**population_params, genome_params=genome_params)
   ea = cgp.ea.MuPlusLambda(**ea_params)
   

4. Define a callback function to record information about the progress of the evolution:

   history = {}
   history["fitness_parents"] = []
   def recording_callback(pop):
       history["fitness_parents"].append(pop.fitness_parents())
   

5. Use the evolve function that ties everything together and executes the evolution:

   cgp.evolve(pop, obj, ea, **evolve_params, print_progress=True, callback=recording_callback)