mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-03-01 20:38:40 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update genetic_algorithm_optimization.py

Browse Source
This commit is contained in:
UTSAV SINGHAL 2024-11-15 14:56:23 +05:30 committed by GitHub
parent dbd29aed76
commit 84b29c0eed
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 17 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
genetic_algorithm/genetic_algorithm_optimization.py
View File

@ -60,10 +60,7 @@ class GeneticAlgorithm:
True True
""" """
return [ return [
rng.uniform( np.array([rng.uniform(b[0], b[1]) for b in self.bounds])
low=[self.bounds[j][0] for j in range(self.dim)],
high=[self.bounds[j][1] for j in range(self.dim)],
)
for _ in range(self.population_size) for _ in range(self.population_size)
] ]
@ -122,6 +119,10 @@ class GeneticAlgorithm:
# Parent 2 should be [-1.0, -2.0] # Parent 2 should be [-1.0, -2.0]
True True
""" """
if not population_score:
raise ValueError("Population score is empty, cannot select parents.")
population_score.sort(key=lambda score_tuple: score_tuple[1], reverse=True) population_score.sort(key=lambda score_tuple: score_tuple[1], reverse=True)
selected_count = min(N_SELECTED, len(population_score)) selected_count = min(N_SELECTED, len(population_score))
return [ind for ind, _ in population_score[:selected_count]] return [ind for ind, _ in population_score[:selected_count]]
@ -245,10 +246,12 @@ class GeneticAlgorithm:
# Evaluate population fitness (multithreaded) # Evaluate population fitness (multithreaded)
population_score = self.evaluate_population() population_score = self.evaluate_population()
# Ensure population_score isn't empty
if not population_score:
raise ValueError("Population score is empty. No individuals evaluated.")
# Check the best individual # Check the best individual
best_individual = max( best_individual = max(population_score, key=lambda score_tuple: score_tuple[1])[0]
population_score, key=lambda score_tuple: score_tuple[1]
)[0]
best_fitness = self.fitness(best_individual) best_fitness = self.fitness(best_individual)
# Select parents for next generation # Select parents for next generation
@ -257,7 +260,7 @@ class GeneticAlgorithm:
# Generate offspring using crossover and mutation # Generate offspring using crossover and mutation
for i in range(0, len(parents), 2): for i in range(0, len(parents), 2):
parent1, parent2 = parents[i], parents[(i + 1) % len(parents)] parent1, parent2 = parents[i], parents[(i + 1) % len(parents)] # Wrap around for odd cases
child1, child2 = self.crossover(parent1, parent2) child1, child2 = self.crossover(parent1, parent2)
next_generation.append(self.mutate(child1)) next_generation.append(self.mutate(child1))
next_generation.append(self.mutate(child2)) next_generation.append(self.mutate(child2))