Example demonstrating the use of the caching decorator.

Caches the results of fitness evaluations in a pickle file (‘example_caching_cache.pkl’). To illustrate its practical use, compare the runtime of this script when you first call it vs. the second time and when you comment out the decorator on inner_objective.

import time

import numpy as np

import cgp

We define the target function for this example.

def f_target(x):
    return x ** 2 + x + 1.0

We then define the objective function for the evolutionary algorithm: It consists of an inner objective which we wrap with the caching decorator. This decorator specifies a pickle file that will be used for caching results of fitness evaluations. The inner objective is then used by the objective function to compute (or retrieve from cache) the fitness of the individual.

@cgp.utils.disk_cache(
    "example_caching_cache.pkl", compute_key=cgp.utils.compute_key_from_sympy_expr_and_args
)
def inner_objective(ind):
    """The caching decorator uses the function parameters to identify
    identical function calls. Here, as many different genotypes
    produce the same simplified SymPy expression we can use these
    avoid reevaluating functionally identical individuals. Note that
    caching only makes sense for deterministic objective functions, as
    it assumes that identical expressions will always return the same
    fitness values.

    """
    expr = ind.to_sympy()
    loss = []
    for x0 in np.linspace(-2.0, 2.0, 100):
        y = float(expr.subs({"x_0": x0}).evalf())
        loss.append((f_target(x0) - y) ** 2)

    time.sleep(0.25)  # emulate long fitness evaluation

    return np.mean(loss)


def objective(individual):
    if not individual.fitness_is_None():
        return individual

    individual.fitness = -inner_objective(individual)

    return individual

Next, we define the parameters for the population, the genome of individuals, and the evolutionary algorithm.

params = {
    "population_params": {"n_parents": 10, "seed": 8188211},
    "ea_params": {
        "n_offsprings": 10,
        "tournament_size": 1,
        "mutation_rate": 0.05,
        "n_processes": 1,
    },
    "genome_params": {
        "n_inputs": 1,
        "n_outputs": 1,
        "n_columns": 10,
        "n_rows": 2,
        "levels_back": 2,
        "primitives": (cgp.Add, cgp.Sub, cgp.Mul, cgp.ConstantFloat),
    },
    "evolve_params": {"max_generations": 200, "termination_fitness": -1e-12},
}

We then create a Population instance and instantiate the evolutionary algorithm.

pop = cgp.Population(**params["population_params"], genome_params=params["genome_params"])
ea = cgp.ea.MuPlusLambda(**params["ea_params"])

Finally, we call the evolve method to perform the evolutionary search.

cgp.evolve(pop, objective, ea, **params["evolve_params"], print_progress=True)


print(f"evolved function: {pop.champion.to_sympy()}")

Out:

[2/200] max fitness: -2.360269360269361
[3/200] max fitness: -2.360269360269361
[4/200] max fitness: -2.360269360269361
[5/200] max fitness: -1.0
[6/200] max fitness: -1.0
[7/200] max fitness: -1.0
[8/200] max fitness: -1.0
[9/200] max fitness: -1.0
[10/200] max fitness: -1.0
[11/200] max fitness: -1.0
[12/200] max fitness: -1.0
[13/200] max fitness: -1.0
[14/200] max fitness: -1.0
[15/200] max fitness: -1.0
[16/200] max fitness: -1.0
[17/200] max fitness: -1.0
[18/200] max fitness: -1.0
[19/200] max fitness: -1.0
[20/200] max fitness: -1.0
[21/200] max fitness: -1.0
[22/200] max fitness: -1.0
[23/200] max fitness: -1.0
[24/200] max fitness: -1.0
[25/200] max fitness: -1.0
[26/200] max fitness: -1.0
[27/200] max fitness: -1.0
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[30/200] max fitness: -1.0
[31/200] max fitness: -1.0
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[35/200] max fitness: -1.0
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[40/200] max fitness: -1.0
[41/200] max fitness: -1.0
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[44/200] max fitness: -1.0
[45/200] max fitness: -1.0
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[48/200] max fitness: -1.0
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[50/200] max fitness: -1.0
[51/200] max fitness: -1.0
[52/200] max fitness: -1.0
[53/200] max fitness: -1.0
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[57/200] max fitness: -1.0
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[70/200] max fitness: -1.0
[71/200] max fitness: -1.0
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[80/200] max fitness: -1.0
[81/200] max fitness: -1.0
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[84/200] max fitness: -1.0
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[87/200] max fitness: -1.0
[88/200] max fitness: -1.0
[89/200] max fitness: -1.0
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[91/200] max fitness: -1.0
[92/200] max fitness: -1.0
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[95/200] max fitness: -1.0
[96/200] max fitness: -1.0
[97/200] max fitness: -1.0
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[100/200] max fitness: -1.0
[101/200] max fitness: -1.0
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[120/200] max fitness: -1.0
[121/200] max fitness: -1.0
[122/200] max fitness: -4.930380657631324e-34
evolved function: x_0**2 + x_0 + 1.0