univer-7th-semester/genetic-algorithms
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best for now RMSE: 30.937

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Артём Тищенко
2025-11-07 00:08:08 +03:00
parent cb2b031e9c
commit cfae423f11
4 changed files with 140 additions and 10 deletions
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59
lab4/gp/mutations.py
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@@ -37,3 +37,62 @@ def grow_mutation(chromosome: Chromosome, max_depth: int) -> Chromosome:
chromosome.root = subtree
return chromosome
def node_replacement_mutation(chromosome: Chromosome) -> Chromosome:
"""Мутация замены операции (Node Replacement Mutation).
Выбирает случайный узел с операцией (arity > 0) и заменяет его
на случайную другую операцию той же арности, сохраняя поддеревья.
Если подходящей альтернативы нет — возвращает копию без изменений.
"""
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)
current_arity = target_node.value.arity
same_arity_ops = [
op
for op in chromosome.operations
if op.arity == current_arity and op != target_node.value
]
if not same_arity_ops:
return chromosome
new_operation = random.choice(same_arity_ops)
target_node.value = new_operation
return chromosome
def hoist_mutation(chromosome: Chromosome) -> Chromosome:
"""Hoist-мутация (анти-bloat).
Выбирает случайное поддерево, затем внутри него — случайное поддерево меньшей глубины,
и заменяет исходное поддерево на это внутреннее.
В результате дерево становится короче, сохраняя часть структуры.
"""
chromosome = chromosome.copy()
operation_nodes = [n for n in chromosome.root.list_nodes() if n.value.arity > 0]
if not operation_nodes:
return chromosome
outer_subtree = random.choice(operation_nodes)
outer_nodes = outer_subtree.list_nodes()[1:] # исключаем корень
inner_subtree = random.choice(outer_nodes).copy_subtree()
if outer_subtree.parent:
outer_subtree.parent.replace_child(outer_subtree, inner_subtree)
else:
chromosome.root = inner_subtree
return chromosome

1
lab4/gp/node.py
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@@ -36,6 +36,7 @@ class Node:
return node
def list_nodes(self) -> list[Node]:
"""Список всех узлов поддерева, начиная с текущего (aka depth-first-search)."""
nodes: list[Node] = [self]
for child in self.children:
nodes.extend(child.list_nodes())

60
lab4/gp/selection.py
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@@ -26,3 +26,63 @@ def roulette_selection(population: Population, fitnesses: Fitnesses) -> Populati
selected.append(population[idx])
return selected
def tournament_selection(
population: Population,
fitnesses: Fitnesses,
k: int = 3,
) -> Population:
"""Турнирная селекция.
В каждом турнире случайно выбирается k особей, и побеждает та,
у которой лучшее (наибольшее) значение фитнеса. Для минимизации
значения фитнеса нужно предварительно инвертировать.
Args:
population: список особей (Population)
fitnesses: список или массив фитнесов (Fitnesses)
k: размер турнира
Returns:
Новая популяция того же размера
"""
size = len(population)
selected = []
for _ in range(size):
idxs = np.random.choice(size, size=k, replace=False)
fits = fitnesses[idxs]
winner_idx = idxs[np.argmax(fits)]
selected.append(population[winner_idx])
return selected
def stochastic_tournament_selection(
population: Population,
fitnesses: Fitnesses,
k: int = 3,
p_best: float = 0.75,
) -> Population:
"""Стохастическая турнирная селекция.
Побеждает лучший в турнире с вероятностью p_best, иначе выбирается
случайный участник турнира.
"""
size = len(population)
selected = []
for _ in range(size):
idxs = np.random.choice(size, size=k, replace=False)
fits = fitnesses[idxs]
order = np.argsort(-fits)
if np.random.random() < p_best:
winner_idx = idxs[order[0]]
else:
winner_idx = np.random.choice(idxs[1:]) if k > 1 else idxs[0]
selected.append(population[winner_idx])
return selected

30
lab4/main.py
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@@ -15,16 +15,21 @@ from gp.fitness import (
RMSEFitness,
)
from gp.ga import GARunConfig, genetic_algorithm
from gp.mutations import grow_mutation, shrink_mutation
from gp.mutations import (
grow_mutation,
hoist_mutation,
node_replacement_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
from gp.selection import roulette_selection, tournament_selection
NUM_VARS = 9
TEST_POINTS = 10000
MAX_DEPTH = 13
MAX_GENERATIONS = 500
MAX_DEPTH = 15
MAX_GENERATIONS = 200
np.random.seed(17)
random.seed(17)
X = np.random.uniform(-5.536, 5.536, size=(TEST_POINTS, NUM_VARS))
@@ -81,7 +86,7 @@ def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
# fitness_function = PenalizedFitness(
# target_function, lambda: X, base_fitness=fitness, lambda_=0.003
# )
fitness_function = HuberFitness(target_function, lambda: X)
fitness_function = RMSEFitness(target_function, lambda: X)
# fitness_function = PenalizedFitness(
# target_function, lambda: X, base_fitness=fitness, lambda_=0.003
# )
@@ -103,9 +108,13 @@ def adaptive_mutation(
r = random.random()
# 50% grow, 50% shrink
if r < 0.5:
if r < 0.4:
return grow_mutation(chromosome, max_depth=max_depth)
elif r < 0.7:
return node_replacement_mutation(chromosome)
elif r < 0.85:
return hoist_mutation(chromosome)
return shrink_mutation(chromosome)
@@ -151,14 +160,15 @@ config = GARunConfig(
mutation_fn=lambda chrom, gen_num: adaptive_mutation(
chrom, gen_num, MAX_GENERATIONS, MAX_DEPTH
),
selection_fn=roulette_selection,
# selection_fn=roulette_selection,
selection_fn=lambda p, f: tournament_selection(p, f, k=3),
init_population=ramped_initialization(
15, [4, 5, 6, 6, 7, 7, 8, 9, 10, 11], terminals, operations
10, [4, 5, 6, 6, 7, 7, 8, 9, 10, 11], terminals, operations
),
seed=17,
pc=0.9,
pm=0.3,
elitism=30,
elitism=10,
max_generations=MAX_GENERATIONS,
log_every_generation=True,
)