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run_config.py
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run_config.py
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from itertools import product
drivers = ["SPEA2", "IBEA", "NSGAII", "OMOPSO", "NSGAIII", "JGBL", "SMSEMOA", "NSLS"]
metaalgorithms = ["IMGA", "HGS", "DHGS"]
algorithms = [
"{}+{}".format(meta, algo) for meta, algo in product(metaalgorithms, drivers)
] + drivers
custom_paths = {"DHGS": ("algorithms.HGS.HGS", "HGS")}
problems = [
"ZDT1",
"ZDT2",
"ZDT3",
"ZDT4",
"ZDT6",
"UF1",
"UF2",
"UF3",
"UF4",
"UF5",
"UF6",
"UF7",
"UF8",
"UF9"
]
DEFAULT_POPULATION_SIZE = 64
metaconfig_budgets = list(range(500, 9500, 1000))
class NotViableConfiguration(Exception):
pass
sclng_coeffs = [10, 2.5, 1]
algo_base = {
"IBEA": {"kappa": 0.05, "mating_population_size": 0.5},
"NSGAII": {"mating_population_size": 0.5},
"JGBL": {
"mating_population_size": 0.5,
"jumping_rate": 0.6,
"jumping_percentage": 0.5,
},
"IMGA": {"islands_number": 3, "migrants_number": 5, "epoch_length": 5},
"NSLS": {"local_search_mu": 0.5, "local_search_sigma": 0.5},
"HGS": {
"hgs_type": "classic",
"fitness_errors": (0.0, 0.00, 0.0),
"cost_modifiers": (1.0, 1.0, 1.0),
"mutation_etas": (10.0, 12.0, 15.0),
"crossover_etas": (15.0, 20.0, 25.0),
"population_sizes": (64, 20, 10),
"comparison_multipliers": (1.0, 0.08, 0.020),
"mantissa_bits": (4, 16, 64),
"max_sprouts_no": 16,
"sproutiveness": 3,
"metaepoch_len": [5,5,5],
"min_progress_ratio": [0.0, 0.00001, 0.0001],
},
"DHGS": {
"hgs_type": "distributed",
"fitness_errors": (0.0, 0.00, 0.0),
"cost_modifiers": (1.0, 1.0, 1.0),
"mutation_etas": (10.0, 12.0, 15.0),
"crossover_etas": (15.0, 20.0, 25.0),
"population_sizes": (64, 20, 10),
"comparison_multipliers": (1.0, 0.08, 0.020),
"mantissa_bits": (4, 16, 64),
"max_sprouts_no": 16,
"sproutiveness": 3,
"metaepoch_len": [5,5,5],
"min_progress_ratio": [0.0, 0.00001, 0.0001],
},
}
cust_base = {}
def init_alg___HGS(algo_config, problem_mod):
reference_point = tuple(50.0 for _ in range(len(problem_mod.pareto_front[0])))
algo_config.update(
{
"reference_point": reference_point,
"mutation_rates": [1.0 / len(problem_mod.dims) for _ in range(3)],
"crossover_rates": [0.9 for _ in range(3)],
}
)
init_alg___DHGS = init_alg___HGS
def init_alg___IBEA(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___SPEA2(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___NSGAII(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___NSGAIII(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___NSLS(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___JGBL(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___OMOPSO(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def init_alg___SMSEMOA(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
reference_point = tuple(50.0 for _ in range(len(problem_mod.pareto_front[0])))
algo_config.update({"reference_point": reference_point})
def init_alg___IMGA(algo_config, problem_mod):
standard_variance(algo_config, problem_mod)
def standard_variance(algo_config, problem_mod):
algo_config.update(
{
"mutation_eta": 20.0,
"crossover_eta": 30.0,
"mutation_rate": 1.0 / len(problem_mod.dims),
"crossover_rate": 0.9,
}
)