Ejemplo n.º 1
0
def generate_population(toolbox: base.Toolbox, ccl_objects: dict, options: dict) -> List[gp.PrimitiveTree]:
    """Generate initial population"""

    pop = []
    codes: Set[str] = set()
    pbar = tqdm.tqdm(total=options['population_size'])
    while len(pop) < options['population_size']:
        ind = toolbox.individual()
        if not check_symbol_counts(ind, options):
            continue
        try:
            sympy_expr = generate_sympy_expr(ind, ccl_objects)
            if sympy_expr.has(sympy.zoo, sympy.oo, sympy.nan, sympy.I):
                continue
            sympy_code = str(sympy_expr)
        except RuntimeError:
            continue

        if options['max_constant_allowed'] is not None and not check_max_constant(sympy_expr, options):
            continue

        if options['unique_population']:
            if sympy_code in codes:
                continue

        codes.add(sympy_code)
        ind.sympy_code = sympy_code
        pop.append(ind)
        pbar.update()

    pbar.close()
    return pop
Ejemplo n.º 2
0
# ## Evaluating Fitness

# +
def eval_knapsack(individual):
    weight = 0.0
    value = 0.0
    for package_id in individual:
        weight += packages[package_id]["weight"]
        value += packages[package_id]["value"]
    #if len(individual) > MAX_ITEM or weight > MAX_WEIGHT:
    #    return 10000, 0             # Ensure overweighted bags are dominated
    return weight, value


indv = toolbox.individual()
indv_fitness = eval_knapsack(indv)

print(f"The fitness (weight, monetary value) of knapsack {indv}\nis: \n{indv_fitness}")


# -

# ## Transformations

# ### Crossover
#
# Here we use a different crossover from previous notebooks:
#
# `child1` gets that `parent1` and `parent2` overlap: `child1 = parent1 & parent2`
#