univer-7th-semester/genetic-algorithms
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fitnesses

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This commit is contained in:
Артём Тищенко
2025-11-06 22:50:10 +03:00
parent e6765c9254
commit cc180dc700
12 changed files with 959 additions and 65 deletions
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26
lab4/gp/chromosome.py
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@@ -22,32 +22,6 @@ class Chromosome:
def prune(self, max_depth: int) -> None:
self.root.prune(self.terminals, max_depth)
def shrink_mutation(self) -> None:
"""Усекающая мутация. Заменяет случайно выбранную операцию на случайный терминал."""
operation_nodes = [n for n in self.root.list_nodes() if n.value.arity > 0]
if not operation_nodes:
return
target_node = random.choice(operation_nodes)
target_node.prune(self.terminals, max_depth=1)
def grow_mutation(self, max_depth: int) -> None:
"""Растущая мутация. Заменяет случайно выбранный узел на случайное поддерево."""
target_node = random.choice(self.root.list_nodes())
max_subtree_depth = max_depth - target_node.get_level() + 1
subtree = Chromosome.grow_init(
self.terminals, self.operations, max_subtree_depth
).root
if target_node.parent:
target_node.parent.replace_child(target_node, subtree)
else:
self.root = subtree
def __str__(self) -> str:
"""Строковое представление хромосомы в виде формулы в инфиксной форме."""
return str(self.root)

3
lab4/gp/crossovers.py
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@@ -17,6 +17,9 @@ def crossover_subtree(
child2 = parent2.copy()
# Выбираем случайные узлы, не включая корень
if child1.root.get_depth() <= 1 or child2.root.get_depth() <= 1:
return child1, child2
cut1 = random.choice(child1.root.list_nodes()[1:])
cut2 = random.choice(child2.root.list_nodes()[1:])

149
lab4/gp/fitness.py Normal file
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@@ -0,0 +1,149 @@
from abc import ABC, abstractmethod
from typing import Callable
import numpy as np
from numpy.typing import NDArray
from .chromosome import Chromosome
type FitnessFn = Callable[
[
Chromosome,
NDArray[np.float64],
Callable[[NDArray[np.float64]], NDArray[np.float64]],
],
float,
]
type TargetFunction = Callable[[NDArray[np.float64]], NDArray[np.float64]]
type TestPointsFn = Callable[[], NDArray[np.float64]]
class BaseFitness(ABC):
def __init__(self, target_fn: TargetFunction, test_points_fn: TestPointsFn):
self.target_function = target_fn
self.test_points_fn = test_points_fn
@abstractmethod
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float: ...
def __call__(self, chromosome: Chromosome) -> float:
test_points = self.test_points_fn()
context = {t: test_points[:, i] for i, t in enumerate(chromosome.terminals)}
predicted = chromosome.root.eval(context)
true_values = self.target_function(test_points)
return self.fitness_fn(chromosome, predicted, true_values)
class MSEFitness(BaseFitness):
"""Среднеквадратичная ошибка"""
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float:
return float(np.mean((predicted - true_values) ** 2))
class RMSEFitness(BaseFitness):
"""Корень из среднеквадратичной ошибки"""
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float:
return float(np.sqrt(np.mean((predicted - true_values) ** 2)))
class MAEFitness(BaseFitness):
"""Средняя абсолютная ошибка"""
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float:
return float(np.mean(np.abs(predicted - true_values)))
class HuberFitness(BaseFitness):
"""Huber Loss (компромисс между MSE и MAE)"""
def __init__(
self,
target_fn: TargetFunction,
test_points_fn: TestPointsFn,
delta: float = 1.0,
):
super().__init__(target_fn, test_points_fn)
self.delta = delta
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float:
error = predicted - true_values
mask = np.abs(error) <= self.delta
squared = 0.5 * (error[mask] ** 2)
linear = self.delta * (np.abs(error[~mask]) - 0.5 * self.delta)
huber = np.concatenate([squared, linear])
return float(np.mean(huber))
class NRMSEFitness(BaseFitness):
"""Нормализованный RMSE (масштаб-инвариантен)"""
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float:
denom = np.std(true_values)
if denom == 0:
return 1e6
return float(np.sqrt(np.mean((predicted - true_values) ** 2)) / denom)
class PenalizedFitness(BaseFitness):
"""Фитнес со штрафом за размер и глубину дерева: ошибка + λ * (размер + depth_weight * глубина)"""
def __init__(
self,
target_fn: TargetFunction,
test_points_fn: TestPointsFn,
base_fitness: BaseFitness,
lambda_: float = 0.001,
depth_weight: float = 0.2,
):
super().__init__(target_fn, test_points_fn)
self.base_fitness = base_fitness
self.lambda_ = lambda_
self.depth_weight = depth_weight
def fitness_fn(
self,
chromosome: Chromosome,
predicted: NDArray[np.float64],
true_values: NDArray[np.float64],
) -> float:
base = self.base_fitness.fitness_fn(chromosome, predicted, true_values)
size = chromosome.root.get_size()
depth = chromosome.root.get_depth()
penalty = self.lambda_ * (size + self.depth_weight * depth)
return float(base + penalty)

341
lab4/gp/ga.py Normal file
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@@ -0,0 +1,341 @@
import os
import random
import shutil
import time
from copy import deepcopy
from dataclasses import asdict, dataclass
from typing import Callable
import numpy as np
from matplotlib import pyplot as plt
from numpy.typing import NDArray
from .chromosome import Chromosome
from .types import Fitnesses, Population
type FitnessFn = Callable[[Chromosome], float]
type InitializePopulationFn = Callable[[int], Population]
type CrossoverFn = Callable[[Chromosome, Chromosome], tuple[Chromosome, Chromosome]]
type MutationFn = Callable[[Chromosome, int], Chromosome]
type SelectionFn = Callable[[Population, Fitnesses], Population]
@dataclass(frozen=True)
class GARunConfig:
fitness_func: FitnessFn
crossover_fn: CrossoverFn
mutation_fn: MutationFn
selection_fn: SelectionFn
init_population: Population
pc: float # вероятность кроссинговера
pm: float # вероятность мутации
max_generations: int # максимальное количество поколений
elitism: int = (
0 # сколько лучших особей перенести без изменения в следующее поколение
)
max_best_repetitions: int | None = (
None # остановка при повторении лучшего результата
)
seed: int | None = None # seed для генератора случайных чисел
minimize: bool = True # если True, ищем минимум вместо максимума
save_generations: list[int] | None = (
None # индексы поколений для сохранения графиков
)
results_dir: str = "results" # папка для сохранения графиков
fitness_avg_threshold: float | None = (
None # порог среднего значения фитнес функции для остановки
)
best_value_threshold: float | None = (
None # остановка при достижении значения фитнеса лучше заданного
)
log_every_generation: bool = False # логировать каждое поколение
def save(self, filename: str = "GARunConfig.txt"):
"""Сохраняет конфиг в results_dir."""
os.makedirs(self.results_dir, exist_ok=True)
path = os.path.join(self.results_dir, filename)
with open(path, "w", encoding="utf-8") as f:
for k, v in asdict(self).items():
f.write(f"{k}: {v}\n")
@dataclass(frozen=True)
class Generation:
number: int
best: Chromosome
best_fitness: float
population: Population
fitnesses: Fitnesses
@dataclass(frozen=True)
class GARunResult:
generations_count: int
best_generation: Generation
history: list[Generation]
time_ms: float
def save(self, path: str, filename: str = "GARunResult.txt"):
"""Сохраняет конфиг в results_dir."""
os.makedirs(path, exist_ok=True)
path = os.path.join(path, filename)
with open(path, "w", encoding="utf-8") as f:
for k, v in asdict(self).items():
if k == "history":
continue
if k == "best_generation":
f.write(
f"{k}: Number: {v['number']}, Best Fitness: {v['best_fitness']}, Best: {v['best']}\n"
)
else:
f.write(f"{k}: {v}\n")
def crossover(
population: Population,
pc: float,
crossover_fn: CrossoverFn,
) -> Population:
"""Оператор кроссинговера (скрещивания) выполняется с заданной вероятностью pc.
Две хромосомы (родители) выбираются случайно из промежуточной популяции.
Если популяция нечетного размера, то последняя хромосома скрещивается со случайной
другой хромосомой из популяции. В таком случае одна из хромосом может поучаствовать
в кроссовере дважды.
"""
# Создаем копию популяции и перемешиваем её для случайного выбора пар
shuffled_population = population.copy()
random.shuffle(shuffled_population)
next_population = []
pop_size = len(shuffled_population)
for i in range(0, pop_size, 2):
p1 = shuffled_population[i]
p2 = shuffled_population[(i + 1) % pop_size]
if np.random.random() <= pc:
p1, p2 = crossover_fn(p1, p2)
next_population.append(p1)
next_population.append(p2)
return next_population[:pop_size]
def mutation(
population: Population, pm: float, gen_num: int, mutation_fn: MutationFn
) -> Population:
"""Мутация происходит с вероятностью pm."""
next_population = []
for chrom in population:
next_population.append(
mutation_fn(chrom, gen_num) if np.random.random() <= pm else chrom
)
return next_population
def clear_results_directory(results_dir: str) -> None:
"""Очищает папку с результатами перед началом эксперимента."""
if os.path.exists(results_dir):
shutil.rmtree(results_dir)
os.makedirs(results_dir, exist_ok=True)
def eval_population(population: Population, fitness_func: FitnessFn) -> Fitnesses:
return np.array([fitness_func(chrom) for chrom in population])
def save_generation(
generation: Generation, history: list[Generation], config: GARunConfig
) -> None:
os.makedirs(config.results_dir, exist_ok=True)
fig = plt.figure(figsize=(7, 7))
fig.suptitle(
f"Поколение #{generation.number}. "
f"Лучшая особь: {generation.best_fitness:.0f}. "
f"Среднее значение: {np.mean(generation.fitnesses):.0f}",
fontsize=14,
y=0.95,
)
# Рисуем
...
filename = f"generation_{generation.number:03d}.png"
path_png = os.path.join(config.results_dir, filename)
fig.savefig(path_png, dpi=150, bbox_inches="tight")
plt.close(fig)
def genetic_algorithm(config: GARunConfig) -> GARunResult:
if config.seed is not None:
random.seed(config.seed)
np.random.seed(config.seed)
if config.save_generations:
clear_results_directory(config.results_dir)
population = config.init_population
start = time.perf_counter()
history: list[Generation] = []
best: Generation | None = None
generation_number = 1
best_repetitions = 0
while True:
# Вычисляем фитнес для всех особей в популяции
fitnesses = eval_population(population, config.fitness_func)
# Сохраняем лучших особей для переноса в следующее поколение
elites: list[Chromosome] = []
if config.elitism:
elites = deepcopy(
[
population[i]
for i in sorted(
range(len(fitnesses)),
key=lambda i: fitnesses[i],
reverse=not config.minimize,
)
][: config.elitism]
)
# Находим лучшую особь в поколении
best_index = (
int(np.argmin(fitnesses)) if config.minimize else int(np.argmax(fitnesses))
)
# Добавляем эпоху в историю
current = Generation(
number=generation_number,
best=population[best_index],
best_fitness=fitnesses[best_index],
# population=deepcopy(population),
population=[],
# fitnesses=deepcopy(fitnesses),
fitnesses=np.array([]),
)
history.append(current)
if config.log_every_generation:
print(
f"Generation #{generation_number} best: {current.best_fitness},"
f" avg: {np.mean(fitnesses)}"
)
# Обновляем лучшую эпоху
if (
best is None
or (config.minimize and current.best_fitness < best.best_fitness)
or (not config.minimize and current.best_fitness > best.best_fitness)
):
best = current
# Проверка критериев остановки
stop_algorithm = False
if generation_number >= config.max_generations:
stop_algorithm = True
if config.max_best_repetitions is not None and generation_number > 1:
if history[-2].best_fitness == current.best_fitness:
best_repetitions += 1
if best_repetitions == config.max_best_repetitions:
stop_algorithm = True
else:
best_repetitions = 0
if config.best_value_threshold is not None:
if (
config.minimize and current.best_fitness < config.best_value_threshold
) or (
not config.minimize
and current.best_fitness > config.best_value_threshold
):
stop_algorithm = True
if config.fitness_avg_threshold is not None:
mean_fitness = np.mean(fitnesses)
if (config.minimize and mean_fitness < config.fitness_avg_threshold) or (
not config.minimize and mean_fitness > config.fitness_avg_threshold
):
stop_algorithm = True
# Сохраняем указанные поколения и последнее поколение
if config.save_generations and (
stop_algorithm or generation_number in config.save_generations
):
save_generation(current, history, config)
if stop_algorithm:
break
# селекция (для минимума инвертируем знак)
parents = config.selection_fn(
population, fitnesses if not config.minimize else -fitnesses
)
# кроссинговер попарно
next_population = crossover(parents, config.pc, config.crossover_fn)
# мутация
next_population = mutation(
next_population,
config.pm,
generation_number,
config.mutation_fn,
)
# Вставляем элиту в новую популяцию
population = next_population[: len(population) - config.elitism] + elites
generation_number += 1
end = time.perf_counter()
assert best is not None, "Best was never set"
return GARunResult(
len(history),
best,
history,
(end - start) * 1000.0,
)
def plot_fitness_history(result: GARunResult, save_path: str | None = None) -> None:
"""Рисует график изменения лучших и средних значений фитнеса по поколениям."""
generations = [gen.number for gen in result.history]
best_fitnesses = [gen.best_fitness for gen in result.history]
avg_fitnesses = [np.mean(gen.fitnesses) for gen in result.history]
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(
generations, best_fitnesses, label="Лучшее значение", linewidth=2, color="blue"
)
ax.plot(
generations,
avg_fitnesses,
label="Среднее значение",
linewidth=2,
color="orange",
)
ax.set_xlabel("Поколение", fontsize=12)
ax.set_ylabel("Значение фитнес-функции", fontsize=12)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
if save_path:
fig.savefig(save_path, dpi=150, bbox_inches="tight")
print(f"График сохранен в {save_path}")
else:
plt.show()
plt.close(fig)

36
lab4/gp/mutations.py
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@@ -1,3 +1,39 @@
import random
from .chromosome import Chromosome
def shrink_mutation(chromosome: Chromosome) -> Chromosome:
"""Усекающая мутация. Заменяет случайно выбранную операцию на случайный терминал."""
chromosome = chromosome.copy()
operation_nodes = [n for n in chromosome.root.list_nodes() if n.value.arity > 0]
if not operation_nodes:
return chromosome
target_node = random.choice(operation_nodes)
target_node.prune(chromosome.terminals, max_depth=1)
return chromosome
def grow_mutation(chromosome: Chromosome, max_depth: int) -> Chromosome:
"""Растущая мутация. Заменяет случайно выбранный узел на случайное поддерево."""
chromosome = chromosome.copy()
target_node = random.choice(chromosome.root.list_nodes())
max_subtree_depth = max_depth - target_node.get_level() + 1
subtree = Chromosome.grow_init(
chromosome.terminals, chromosome.operations, max_subtree_depth
).root
if target_node.parent:
target_node.parent.replace_child(target_node, subtree)
else:
chromosome.root = subtree
return chromosome

8
lab4/gp/node.py
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@@ -61,14 +61,18 @@ class Node:
prune_recursive(self, 1)
def get_subtree_depth(self) -> int:
def get_depth(self) -> int:
"""Вычисляет глубину поддерева, начиная с текущего узла."""
return (
max(child.get_subtree_depth() for child in self.children) + 1
max(child.get_depth() for child in self.children) + 1
if self.children
else 1
)
def get_size(self) -> int:
"""Вычисляет размер поддерева, начиная с текущего узла."""
return sum(child.get_size() for child in self.children) + 1
def get_level(self) -> int:
"""Вычисляет уровень узла в дереве (расстояние от корня). Корень имеет уровень 1."""
return self.parent.get_level() + 1 if self.parent else 1

4
lab4/gp/ops.py
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@@ -7,6 +7,7 @@ type Value = NDArray[np.float64]
# Унарные операции
NEG = Operation("-", 1, lambda x: -x[0])
SQUARE = Operation("pow2", 1, lambda x: x[0] ** 2)
SIN = Operation("sin", 1, lambda x: np.sin(x[0]))
COS = Operation("cos", 1, lambda x: np.cos(x[0]))
@@ -53,6 +54,3 @@ def _safe_pow(a: Value, b: Value) -> Value:
POW = Operation("^", 2, lambda x: _safe_pow(x[0], x[1]))
# Все операции в либе
ALL = (NEG, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW)

28
lab4/gp/selection.py Normal file
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@@ -0,0 +1,28 @@
import numpy as np
from .types import Fitnesses, Population
def roulette_selection(population: Population, fitnesses: Fitnesses) -> Population:
"""Селекция методом рулетки.
Чем больше значение фитнеса, тем больше вероятность выбора особи. Для минимизации
значения фитнеса нужно предварительно инвертировать.
"""
# Чтобы работать с отрицательными f, сдвигаем значения фитнес функции на минимальное
# значение в популяции. Вычитаем min_fit, т. к. min_fit может быть отрицательным.
min_fit = np.min(fitnesses)
shifted_fitnesses = fitnesses - min_fit + 1e-12
# Получаем вероятности для каждой особи
probs = shifted_fitnesses / np.sum(shifted_fitnesses)
cum = np.cumsum(probs)
# Выбираем особей методом рулетки
selected = []
for _ in population:
r = np.random.random()
idx = int(np.searchsorted(cum, r, side="left"))
selected.append(population[idx])
return selected

1
lab4/gp/types.py
View File

@@ -9,6 +9,7 @@ if TYPE_CHECKING:
from .primitive import Primitive
type Population = list[Chromosome]
type Fitnesses = NDArray[np.float64]
type InitFunc = Callable[[Chromosome], Node]
type Value = NDArray[np.float64]

215
lab4/main.py
View File

@@ -1,36 +1,185 @@
import random
from math import log
import numpy as np
from numpy.typing import NDArray
from gp import Chromosome, ops
from gp.population import ramped_initialization
from gp.primitive import Var
operations = ops.ALL
terminals = [Var(f"x{i}") for i in range(1, 9)]
chrom = Chromosome.full_init(terminals, operations, max_depth=3)
print("Depth:", chrom.root.get_subtree_depth())
print("Formula:", chrom)
print("Tree:\n", chrom.root.to_str_tree())
values = [
np.array([1.0]),
np.array([2.0]),
np.array([3.0]),
np.array([4.0]),
np.array([5.0]),
np.array([6.0]),
np.array([7.0]),
np.array([8.0]),
]
context = {var: value for var, value in zip(terminals, values)}
print("Value for ", values, ":", chrom.root.eval(context))
population = ramped_initialization(
5,
[3, 4, 5, 6, 7, 8],
terminals,
operations,
from gp import Chromosome
from gp.crossovers import crossover_subtree
from gp.fitness import (
HuberFitness,
MAEFitness,
MSEFitness,
NRMSEFitness,
PenalizedFitness,
RMSEFitness,
)
print("Population size:", len(population))
print("Population:")
[print(str(chrom)) for chrom in population]
from gp.ga import GARunConfig, genetic_algorithm
from gp.mutations import grow_mutation, shrink_mutation
from gp.ops import ADD, COS, DIV, EXP, MUL, NEG, POW, SIN, SQUARE, SUB
from gp.population import ramped_initialization
from gp.primitive import Const, Var
from gp.selection import roulette_selection
NUM_VARS = 9
TEST_POINTS = 10000
MAX_DEPTH = 13
MAX_GENERATIONS = 500
np.random.seed(17)
random.seed(17)
X = np.random.uniform(-5.536, 5.536, size=(TEST_POINTS, NUM_VARS))
# axes = [np.linspace(-5.536, 5.536, TEST_POINTS) for _ in range(NUM_VARS)]
# X = np.array(np.meshgrid(*axes)).T.reshape(-1, NUM_VARS)
operations = [SQUARE, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW]
# operations = [SQUARE, ADD, SUB, MUL]
terminals = [Var(f"x{i}") for i in range(1, NUM_VARS + 1)]
# def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
# """
# f(x) = x1 + x2 + sin(x1)
# x имеет форму (n_samples, n_vars)
# """
# x1 = x[:, 0]
# x2 = x[:, 1]
# return x1 + x2 + np.sin(x1) + np.sin(x2) + np.exp(-x1) + np.cos(x2)
# def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
# """
# Простая тестовая функция: сумма косинусов всех переменных.
# f(x) = sum_i cos(x_i)
# x имеет форму (n_samples, n_vars)
# """
# return np.sum(x, axis=1)
def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
"""
Векторизованная версия функции:
f(x) = sum_{i=1}^n sum_{j=1}^i x_j^2
x имеет форму (n_samples, n_vars)
"""
# Префиксные суммы квадратов по оси переменных
x_sq = x**2
prefix_sums = np.cumsum(x_sq, axis=1)
# Суммируем по i (ось 1)
return np.sum(prefix_sums, axis=1)
# def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
# """
# Rastrigin function.
# f(x) = 10 * n + sum(x_i^2 - 10 * cos(2πx_i))
# x: shape (n_samples, n_vars)
# """
# n = x.shape[1]
# return 10 * n + np.sum(x**2 - 10 * np.cos(2 * np.pi * x), axis=1)
# fitness_function = MSEFitness(target_function, lambda: X)
# fitness = HuberFitness(target_function, lambda: X, delta=1.0)
# fitness_function = PenalizedFitness(
# target_function, lambda: X, base_fitness=fitness, lambda_=0.003
# )
fitness_function = HuberFitness(target_function, lambda: X)
# fitness_function = PenalizedFitness(
# target_function, lambda: X, base_fitness=fitness, lambda_=0.003
# )
def adaptive_mutation(
chromosome: Chromosome,
generation: int,
max_generations: int,
max_depth: int,
) -> Chromosome:
"""Адаптивная мутация.
Меняет вероятность типов мутации по ходу эволюции:
- Ранняя фаза (<30%): 70% grow, 30% shrink
- Средняя фаза (30–70%): 40% grow, 60% shrink
- Поздняя фаза (>=70%): 20% grow, 80% shrink
"""
r = random.random()
# 50% grow, 50% shrink
if r < 0.5:
return grow_mutation(chromosome, max_depth=max_depth)
return shrink_mutation(chromosome)
# def adaptive_mutation(
# chromosome: Chromosome,
# generation: int,
# max_generations: int,
# max_depth: int,
# ) -> Chromosome:
# """Адаптивная мутация.
# Меняет вероятность типов мутации по ходу эволюции:
# - Ранняя фаза (<30%): 70% grow, 30% shrink
# - Средняя фаза (30–70%): 40% grow, 60% shrink
# - Поздняя фаза (>=70%): 20% grow, 80% shrink
# """
# # Вычисляем прогресс в диапазоне [0, 1]
# if max_generations <= 0:
# progress = 0.0
# else:
# progress = min(1.0, max(0.0, generation / max_generations))
# r = random.random()
# # Определяем тип мутации
# if progress < 0.3:
# do_grow = r < 0.7
# elif progress < 0.7:
# do_grow = r < 0.4
# else:
# do_grow = r < 0.2
# # Выполняем выбранную мутацию
# if do_grow:
# return grow_mutation(chromosome, max_depth=max_depth)
# return shrink_mutation(chromosome)
config = GARunConfig(
fitness_func=fitness_function,
crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=8),
mutation_fn=lambda chrom, gen_num: adaptive_mutation(
chrom, gen_num, MAX_GENERATIONS, MAX_DEPTH
),
selection_fn=roulette_selection,
init_population=ramped_initialization(
15, [4, 5, 6, 6, 7, 7, 8, 9, 10, 11], terminals, operations
),
seed=17,
pc=0.9,
pm=0.3,
elitism=30,
max_generations=MAX_GENERATIONS,
log_every_generation=True,
)
result = genetic_algorithm(config)
# Выводим результаты
print(f"Лучшая особь: {result.best_generation.best}")
print(result.best_generation.best.root.to_str_tree())
print(f"Лучшее значение фитнеса: {result.best_generation.best_fitness:.6f}")
print(f"Количество поколений: {result.generations_count}")
print(f"Время выполнения: {result.time_ms:.2f} мс")
mse_fitness = MSEFitness(target_function, lambda: X)
print(f"MSE: {mse_fitness(result.best_generation.best):.6f}")
rmse_fitness = RMSEFitness(target_function, lambda: X)
print(f"RMSE: {rmse_fitness(result.best_generation.best):.6f}")
mae_fitness = MAEFitness(target_function, lambda: X)
print(f"MAE: {mae_fitness(result.best_generation.best):.6f}")
huber_fitness = HuberFitness(target_function, lambda: X, delta=1.0)
print(f"Huber: {huber_fitness(result.best_generation.best):.6f}")
nrmse_fitness = NRMSEFitness(target_function, lambda: X)
print(f"NRMSE: {nrmse_fitness(result.best_generation.best):.6f}")

1
lab4/pyproject.toml
View File

@@ -3,6 +3,7 @@ name = "lab4"
version = "0.1.0"
requires-python = ">=3.14"
dependencies = [
"matplotlib>=3.10.7",
"numpy>=2.3.4",
]

212
lab4/uv.lock generated
View File

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