RuntimeError: All individuals are invalid

[TuuSiwei]

[2024-07-22 19:57:06,333][root][INFO] - Workspace: D:\deeplearning\LLM-as-HH\outputs\main\2024-07-22_19-57-06
[2024-07-22 19:57:06,333][root][INFO] - Project Root: D:\deeplearning\LLM-as-HH
[2024-07-22 19:57:06,333][root][INFO] - Using LLM: llama3-70b
[2024-07-22 19:57:06,333][root][INFO] - Using Algorithm: reevo
[2024-07-22 19:57:07,393][root][INFO] - Problem: tsp_aco
[2024-07-22 19:57:07,393][root][INFO] - Problem description: Solving Traveling Salesman Problem (TSP) via stochastic solution sampling following "heuristics". TSP requires finding the shortest path that visits all given nodes and returns to the starting node.
[2024-07-22 19:57:07,393][root][INFO] - Function name: heuristics
[2024-07-22 19:57:07,394][root][INFO] - Evaluating seed function...
[2024-07-22 19:57:07,395][root][INFO] - Seed function code:
import numpy as np
def heuristics_v2(distance_matrix: np.ndarray) -> np.ndarray:
return 1 / distance_matrix
[2024-07-22 19:57:07,395][root][INFO] - Iteration 0: Running Code 0
[2024-07-22 19:57:09,359][root][INFO] - Iteration 0: Code Run 0 successful!
[2024-07-22 19:57:16,735][root][INFO] - Iteration 0, response_id 0: Objective value: 6.469335357675092
[2024-07-22 19:57:16,735][root][INFO] - Iteration 0: Elitist: 6.469335357675092
[2024-07-22 19:57:16,735][root][INFO] - Iteration 0 finished...
[2024-07-22 19:57:16,735][root][INFO] - Best obj: 6.469335357675092, Best Code Path: problem_iter0_code0.py
[2024-07-22 19:57:16,735][root][INFO] - Function Evals: 1
[2024-07-22 19:57:16,735][root][INFO] - Initial Population Prompt:
System Prompt:
You are an expert in the domain of optimization heuristics. Your task is to design heuristics that can effectively solve optimization problems.
Your response outputs Python code and nothing else. Format your code as a Python code string: "python ... ".

User Prompt:
Write a heuristics function for Solving Traveling Salesman Problem (TSP) via stochastic solution sampling following "heuristics". TSP requires finding the shortest path that visits all given nodes and returns to the starting node.
The heuristics function takes as input a distance matrix, and returns prior indicators of how promising it is to include each edge in a solution. The return is of the same shape as the input.

def heuristics_v1(distance_matrix: np.ndarray) -> np.ndarray:
return 1 / distance_matrix

Refer to the format of a trivial design above. Be very creative and give heuristics_v2. Output code only and enclose your code with Python code block: python ... .

• Try combining various factors to determine how promising it is to select an edge.
• Try sparsifying the matrix by setting unpromising elements to zero.
  [2024-07-22 19:57:23,014][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:24,906][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:24,912][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:25,154][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:25,890][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:25,937][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:25,944][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:25,956][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:27,468][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:27,642][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:27,867][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:27,995][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:28,010][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:28,103][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:28,382][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:28,957][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:28,960][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:29,043][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:29,156][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:29,326][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:29,465][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:29,524][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:29,927][openai._base_client][INFO] - Retrying request to /chat/completions in 0.782544 seconds
  [2024-07-22 19:57:30,286][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:30,678][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:32,469][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:57:33,093][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:58:02,649][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:58:05,625][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:58:05,737][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:58:07,129][httpx][INFO] - HTTP Request: POST https://api.llama-api.com/chat/completions "HTTP/1.1 200 OK"
  [2024-07-22 19:58:07,143][root][INFO] - Iteration 1: Running Code 13
  [2024-07-22 19:58:08,875][root][INFO] - Iteration 1: Code Run 13 execution error!
  Error executing job with overrides: []
  Traceback (most recent call last):
  File "D:\deeplearning\LLM-as-HH\main.py", line 59, in
  main()
  File "E:\conda\envs\gnn310\lib\site-packages\hydra\main.py", line 94, in decorated_main
  _run_hydra(
  File "E:\conda\envs\gnn310\lib\site-packages\hydra_internal\utils.py", line 394, in _run_hydra
  _run_app(
  File "E:\conda\envs\gnn310\lib\site-packages\hydra_internal\utils.py", line 457, in _run_app
  run_and_report(
  File "E:\conda\envs\gnn310\lib\site-packages\hydra_internal\utils.py", line 223, in run_and_report
  raise ex
  File "E:\conda\envs\gnn310\lib\site-packages\hydra_internal\utils.py", line 220, in run_and_report
  return func()
  File "E:\conda\envs\gnn310\lib\site-packages\hydra_internal\utils.py", line 458, in
  lambda: hydra.run(
  File "E:\conda\envs\gnn310\lib\site-packages\hydra_internal\hydra.py", line 132, in run
  _ = ret.return_value
  File "E:\conda\envs\gnn310\lib\site-packages\hydra\core\utils.py", line 260, in return_value
  raise self._return_value
  File "E:\conda\envs\gnn310\lib\site-packages\hydra\core\utils.py", line 186, in run_job
  ret.return_value = task_function(task_cfg)
  File "D:\deeplearning\LLM-as-HH\main.py", line 38, in main
  best_code_overall, best_code_path_overall = lhh.evolve()
  File "D:\deeplearning\LLM-as-HH\reevo.py", line 443, in evolve
  raise RuntimeError(f"All individuals are invalid. Please check the stdout files in {os.getcwd()}.")
  RuntimeError: All individuals are invalid. Please check the stdout files in D:\deeplearning\LLM-as-HH\outputs\main\2024-07-22_19-57-06.
  [2024-07-22 19:58:09,050][root][INFO] - Iteration 1, response_id 13: Objective value: inf
  [2024-07-22 19:58:09,050][root][INFO] - Iteration 1 finished...
  [2024-07-22 19:58:09,050][root][INFO] - Best obj: 6.469335357675092, Best Code Path: problem_iter0_code0.py
  [2024-07-22 19:58:09,050][root][INFO] - Function Evals: 31

How to solve the problem？

[henry-yeh]

Thank you for your interest.

RuntimeError: All individuals are invalid. Please check the stdout files in D:\deeplearning\LLM-as-HH\outputs\main\2024-07-22_19-57-06.

Please check out the stdout files accordingly to see why all generated individuals are invalid.