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improve_algo_creation.txt
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improve_algo_creation.txt
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import gc
from multiprocessing import Pool
from threading import Thread
import openai # Assuming OpenAI's GPT model for solution generation
# Define the dynamic utility function
def dynamic_utility_function(solution, context):
# Implement logic to dynamically evaluate the solution
return computed_utility
# Real solution generation with a language model
def generate_solution_with_language_model(problem_statement, hints, context):
prompt = f"Problem: {problem_statement}\nHints: {hints}\nContext: {context}\nSolution:"
response = openai.Completion.create(
engine="text-davinci-004",
prompt=prompt,
max_tokens=150
)
return response.choices[0].text.strip()
# Iterative improvement with error handling
def iterative_improvement(initial_solution, language_model, utility_function, n_iterations=5):
best_solution = initial_solution
for _ in range(n_iterations):
try:
improved_solution = generate_solution_with_language_model(
initial_solution, "Optimization hints", "Context information"
)
if utility_function(improved_solution, "Context information") > utility_function(best_solution, "Context information"):
best_solution = improved_solution
except Exception as e:
print(f"Error during iteration: {e}")
gc.collect()
continue
return best_solution
# Parallel solution generation using multiprocessing
def parallel_solution_generation(initial_solutions, language_model, utility_function, n_processes=4):
with Pool(processes=n_processes) as pool:
results = [pool.apply_async(iterative_improvement, args=(solution, language_model, utility_function)) for solution in initial_solutions]
improved_solutions = [result.get() for result in results]
return improved_solutions
# Main function to tie everything together
def main():
initial_solutions = ["Solution 1", "Solution 2", "Solution 3"] # Example initial solutions
language_model = "GPT-4" # Placeholder for actual language model
utility_function = dynamic_utility_function # The dynamic utility function
# Choose either parallel_solution_generation or iterative_improvement based on your needs
best_solutions = parallel_solution_generation(initial_solutions, language_model, utility_function)
print("Best Solutions:", best_solutions)
if __name__ == "__main__":
main()