Best program is the same as the initial one #281
Description
I am running the example function_minimization and the problem I'm facing is that the best model is identical to initial_program.py, although it seems that the LLM produces meaningful responses.
What I did 🧐
I ran
export OPENAI_API_KEY=
python openevolve-run.py examples/function_minimization/initial_program.py \
examples/function_minimization/evaluator.py \
--config examples/function_minimization/config.yaml \
--iterations 50Upon completion I get:
Evolution complete!
Best program metrics:
runs_successfully: 1.0000
value_score: 0.9766
distance_score: 0.8626
combined_score: 1.4206
reliability_score: 1.0000
The issue 🐞
The output I'm getting (i.e., the file examples/function_minimization/openevolve_output/best/best_program.py) is the same as initial_program.py.
The LLMs seems to work fine. For example, these are the contents of the file `openevolve_output/checkpoints/checkpoint_50/programs/
{
"id": "f75e030b-9f75-4f3f-a694-f53191189574",
"code": "# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n # Use acceptance criteria more sophisticated than just comparing the values directly. Consider factors like the distance from the current best solution to explore a wider search space\n# Simulated Annealing Implementation\n temperature = 1.0 \n acceptance_probability = np.exp((best_value - value) / temperature) \n if np.random.rand() < acceptance_probability or value < best_value:\n best_value = value \n best_x, best_y = x, y\n\n temperature *= 0.99 # Cooling Schedule\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n",
"language": "python",
"parent_id": "98bd9cda-b42f-4580-96e0-ed12726f1a70",
"generation": 4,
"timestamp": 1759850904.266345,
"iteration_found": 36,
"metrics": {
"runs_successfully": 0,
"combined_score": 0,
"error": "unexpected indent (tmplc616pp6.py, line 37)"
},
"complexity": 0,
"diversity": 0,
"metadata": {
"changes": "Change 1: Replace if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement with 8 lines",
"parent_metrics": {
"runs_successfully": 1,
"value_score": 0.3918292360326052,
"distance_score": 0.1762379470105086,
"combined_score": 0.24447417633901325
},
"island": 0
},
"prompts": {
"diff_user": {
"system": "You are an expert programmer specializing in optimization algorithms. Your task is to improve a function minimization algorithm to find the global minimum of a complex function with many local minima. The function is f(x, y) = sin(x) * cos(y) + sin(x*y) + (x^2 + y^2)/20. Focus on improving the search_algorithm function to reliably find the global minimum, escaping local minima that might trap simple algorithms.",
"user": "# Current Program Information\n- Fitness: 0.2445\n- Feature coordinates: No feature coordinates\n- Focus areas: - Fitness declined: 1.0151 → 0.2445. Consider revising recent changes.\n- Consider simplifying - code length exceeds 500 characters\n\n## Last Execution Output\n\n### stage1_result\n```\nFound solution at x=1.2071, y=-2.9789 with value=0.0331\n```\n\n### distance_to_global\n```\n4.6741\n```\n\n### solution_quality\n```\nCould be improved\n```\n\n# Program Evolution History\n## Previous Attempts\n\n### Attempt 3\n- Changes: Change 1: Replace 3 lines with 18 lines\nChange 2: Replace 2 lines with 4 lines\n- Metrics: runs_successfully: 1.0000, value_score: 0.7397, distance_score: 0.7032, combined_score: 1.0151\n- Outcome: Mixed results\n\n### Attempt 2\n- Changes: Unknown changes\n- Metrics: runs_successfully: 1.0000, value_score: 0.9418, distance_score: 0.7566, combined_score: 1.2148\n- Outcome: Improvement in all metrics\n\n### Attempt 1\n- Changes: Change 1: Replace 2 lines with 8 lines\nChange 2: Replace if value < best_value: # Accept new values if they're strictly better with 6 lines\n- Metrics: runs_successfully: 1.0000, value_score: 0.9766, distance_score: 0.8626, combined_score: 1.4206, reliability_score: 1.0000\n- Outcome: Mixed results\n\n## Top Performing Programs\n\n### Program 1 (Score: 1.4206)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n if value < best_value:\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nKey features: Performs well on runs_successfully (1.0000), Performs well on value_score (0.9766), Performs well on distance_score (0.8626), Performs well on combined_score (1.4206), Performs well on reliability_score (1.0000)\n\n### Program 2 (Score: 1.2148)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n if value < best_value:\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nKey features: Performs well on runs_successfully (1.0000), Performs well on value_score (0.9418), Performs well on distance_score (0.7566), Performs well on combined_score (1.2148)\n\n### Program 3 (Score: 1.0151)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n # Introduction of a step size parameter for exploring the search space more effectively\n step_size = 0.2 \n x = best_x + np.random.uniform(-step_size, step_size) \n y = best_y + np.random.uniform(-step_size, step_size)\n value = evaluate_function(x, y)\n\n # Use acceptance criteria more sophisticated than just comparing the values directly. Consider factors like the distance from the current best solution to explore a wider search space\n if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nKey features: Performs well on runs_successfully (1.0000), Performs well on value_score (0.7397), Performs well on distance_score (0.7032), Performs well on combined_score (1.0151)\n\n\n\n## Diverse Programs\n\n### Program D1 (Score: 0.3940)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n # Implement early stopping condition here. An example using a simple threshold:\n best_value_prev = best_value \n for _ in range(iterations): \n if best_value - best_value_prev < 0.01: # Adjust threshold as needed \n break\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n # Use acceptance criteria more sophisticated than just comparing the values directly. Consider factors like the distance from the current best solution to explore a wider search space\n if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nKey features: Alternative approach to runs_successfully, Alternative approach to value_score\n\n### Program D2 (Score: 0.7216)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n # Use acceptance criteria more sophisticated than just comparing the values directly. Consider factors like the distance from the current best solution to explore a wider search space\n if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nKey features: Alternative approach to runs_successfully, Alternative approach to value_score\n\n## Inspiration Programs\n\nThese programs represent diverse approaches and creative solutions that may inspire new ideas:\n\n### Inspiration 1 (Score: 0.0000, Type: Exploratory)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n\n temperature = initial_temperature # Set a starting temperature\n\n for _ in range(iterations):\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n delta_value = value - best_value \n\n if delta_value < 0 or np.exp(-delta_value / temperature) > np.random.rand(): # Accept based on temperature and random chance\n best_value = value\n best_x, best_y = x, y\n\n temperature *= cooling_rate # Gradually decrease the temperature\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nUnique approach: Modification: Change 1: Replace 9 lines with 14 lines, Alternative runs_successfully approach, Alternative combined_score approach\n\n### Inspiration 2 (Score: 0.1482, Type: Exploratory)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n # Use acceptance criteria more sophisticated than just comparing the values directly. Consider factors like the distance from the current best solution to explore a wider search space\n if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nUnique approach: Modification: Change 1: Replace 2 lines with 4 lines\nChange 2: Replace 3 lines with 4 lines, Excellent runs_successfully (1.000), Alternative value_score approach\n\n### Inspiration 3 (Score: 0.0000, Type: Exploratory)\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Implement a more sophisticated search strategy like Simulated Annealing or Particle Swarm Optimization\n # Example using simulated annealing (conceptual):\n temperature = 1.0 # Initial temperature\n acceptance_rate = 0.5 \n\n while temperature > 0.01: # Decrease temperature gradually \n x_new = x + np.random.uniform(-0.1, 0.1)\n y_new = y + np.random.uniform(-0.1, 0.1) # Small random perturbations\n\n value_new = evaluate_function(x_new, y_new) \n\n if value_new < best_value: \n best_value = value_new\n best_x, best_y = x_new, y_new\n else:\n acceptance_probability = np.exp((best_value - value_new) / temperature) # Metropolis criterion\n if np.random.rand() < acceptance_probability: \n x, y = x_new, y_new\n\n temperature *= 0.99 # Cool down the system\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\nUnique approach: Modification: Change 1: Replace 8 lines with 20 lines, Alternative runs_successfully approach, Alternative combined_score approach\n\n# Current Program\n```python\n# EVOLVE-BLOCK-START\n\"\"\"Function minimization example for OpenEvolve\"\"\"\nimport numpy as np\n\n\ndef search_algorithm(iterations=1000, bounds=(-5, 5)):\n \"\"\"\n A simple random search algorithm that often gets stuck in local minima.\n\n Args:\n iterations: Number of iterations to run\n bounds: Bounds for the search space (min, max)\n\n Returns:\n Tuple of (best_x, best_y, best_value)\n \"\"\"\n # Initialize with a random point\n best_x = np.random.uniform(bounds[0], bounds[1])\n best_y = np.random.uniform(bounds[0], bounds[1])\n best_value = evaluate_function(best_x, best_y)\n\n for _ in range(iterations):\n # Simple random search\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1])\n value = evaluate_function(x, y)\n\n # Use acceptance criteria more sophisticated than just comparing the values directly. Consider factors like the distance from the current best solution to explore a wider search space\n if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement\n best_value = value\n best_x, best_y = x, y\n\n return best_x, best_y, best_value\n\n\n# EVOLVE-BLOCK-END\n\n\n# This part remains fixed (not evolved)\ndef evaluate_function(x, y):\n \"\"\"The complex function we're trying to minimize\"\"\"\n return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20\n\n\ndef run_search():\n x, y, value = search_algorithm()\n return x, y, value\n\n\nif __name__ == \"__main__\":\n x, y, value = run_search()\n print(f\"Found minimum at ({x}, {y}) with value {value}\")\n\n```\n\n# Task\nSuggest improvements to the program that will improve its FITNESS SCORE.\nThe system maintains diversity across these dimensions: complexity, diversity\nDifferent solutions with similar fitness but different features are valuable.\n\nYou MUST use the exact SEARCH/REPLACE diff format shown below to indicate changes:\n\n<<<<<<< SEARCH\n# Original code to find and replace (must match exactly)\n=======\n# New replacement code\n>>>>>>> REPLACE\n\nExample of valid diff format:\n<<<<<<< SEARCH\nfor i in range(m):\n for j in range(p):\n for k in range(n):\n C[i, j] += A[i, k] * B[k, j]\n=======\n# Reorder loops for better memory access pattern\nfor i in range(m):\n for k in range(n):\n for j in range(p):\n C[i, j] += A[i, k] * B[k, j]\n>>>>>>> REPLACE\n\nYou can suggest multiple changes. Each SEARCH section must exactly match code in the current program.\nBe thoughtful about your changes and explain your reasoning thoroughly.\n\nIMPORTANT: Do not rewrite the entire program - focus on targeted improvements.",
"responses": [
"Here are some suggestions to improve the Python program's fitness score. The goal is to make the search algorithm more efficient and effective at finding a lower value for the target function:\n\n**Change 1: Implement Simulated Annealing**\n\nThe current program uses a simple random acceptance criteria, which can get stuck in local minima. Implementing Simulated Annealing can help escape these local minima by allowing occasional \"worse\" moves with a decreasing probability based on temperature.\n\n\n<<<<<<< SEARCH\n if (value < best_value) or ((abs(x - best_x) > 0.1) and (abs(y - best_y) > 0.1)): # Example: Accept if value is better OR significant displacement\n\n \n=======\n# Simulated Annealing Implementation\n temperature = 1.0 \n acceptance_probability = np.exp((best_value - value) / temperature) \n if np.random.rand() < acceptance_probability or value < best_value:\n best_value = value \n best_x, best_y = x, y\n\n temperature *= 0.99 # Cooling Schedule\n\n\n\n>>>>>>> REPLACE\n\n\n**Reasoning:** Simulated Annealing provides a probabilistic mechanism to escape local optima by allowing worse solutions with a decreasing probability over time. This can lead to finding globally better solutions compared to the simple random acceptance criteria used before.\n\n**Change 2: Adaptive Step Size**\n\nInstead of using fixed step sizes for perturbations (like `np.random.uniform(-0.1, 0.1)`), implement an adaptive step size mechanism that adjusts based on the progress made during the search. This could involve reducing the step size when approaching a minimum or increasing it when exploration is necessary.\n\n\n\n<<<<<<< SEARCH\n x = np.random.uniform(bounds[0], bounds[1])\n y = np.random.uniform(bounds[0], bounds[1]) # Simple random search\n\n======= \n #Adaptive Step Size \n if best_value == 0:\n step_size = 0.2\n else:\n step_size = 0.1 \n\n\n x = best_x + np.random.uniform(-step_size, step_size)\n y = best_y + np.random.uniform(-step_size, step_size) \n\n\n\n\n>>>>>>> REPLACE\n\n**Reasoning:** Adaptive step sizes can improve exploration and convergence. Large steps are helpful for early exploration, while smaller steps are beneficial for fine-tuning near a minimum.\n\n\n\n**Additional Notes:**\n* **Preprocessing/Feature Engineering:** While this program doesn't explicitly handle preprocessing or feature engineering, exploring ways to transform the input data (`x` and `y`) could potentially lead to better performance.\n* **Hyperparameter Tuning:** \n The values for `iterations`, `bounds`, `step_size` (if implemented) and even the cooling schedule in simulated annealing can significantly influence the fitness score. Experimenting with different values is recommended to find the optimal combination.\n\n\n\nBy implementing these changes, you aim to enhance your program's ability to navigate the search space effectively, escape local optima, and ultimately find a lower minimum value for the target function."
]
}
},
"artifacts_json": "{\"error_type\": \"IndentationError\", \"error_message\": \"Stage 1 outer exception: unexpected indent (tmplc616pp6.py, line 37)\", \"full_traceback\": \"Traceback (most recent call last):\\n File \\\"/Users/pantelis/Documents/Development/EVOLVE/openevolve/examples/function_minimization/evaluator.py\\\", line 271, in evaluate_stage1\\n spec.loader.exec_module(program)\\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\\n File \\\"<frozen importlib._bootstrap_external>\\\", line 1022, in exec_module\\n File \\\"<frozen importlib._bootstrap_external>\\\", line 1160, in get_code\\n File \\\"<frozen importlib._bootstrap_external>\\\", line 1090, in source_to_code\\n File \\\"<frozen importlib._bootstrap>\\\", line 488, in _call_with_frames_removed\\n File \\\"/var/folders/_w/jx0_4ny55dv0n3t2vyzpcrmw0000gn/T/tmplc616pp6.py\\\", line 37\\n best_value = value\\nIndentationError: unexpected indent\\n\", \"suggestion\": \"Critical error during stage 1 evaluation. Check program syntax and imports\"}",
"artifact_dir": null
}Note that the LLM recommends simulated annealing.
My configuration ⚙️
This is my config.yaml:
# Configuration for function minimization example
# -------------------------------------------------------
max_iterations: 50
checkpoint_interval: 5
# LLM configuration
# -------------------------------------------------------
llm:
primary_model: "codellama:7b"
primary_model_weight: 0.5
secondary_model: "gemma2:9b"
secondary_model_weight: 0.5
api_base: "http://localhost:11434/v1"
api_key: null
temperature: 1.0
max_tokens: 16000
timeout: 120
# Prompt configuration
# -------------------------------------------------------
prompt:
system_message: "You are an expert programmer specializing in optimization algorithms. Your task is to improve a function minimization algorithm to find the global minimum of a complex function with many local minima. The function is f(x, y) = sin(x) * cos(y) + sin(x*y) + (x^2 + y^2)/20. Focus on improving the search_algorithm function to reliably find the global minimum, escaping local minima that might trap simple algorithms."
# Database configuration
# -------------------------------------------------------
database:
population_size: 50
archive_size: 20
num_islands: 3
elite_selection_ratio: 0.2
exploitation_ratio: 0.7
# Evaluator configuration
# -------------------------------------------------------
evaluator:
timeout: 60
cascade_thresholds: [1.3]
parallel_evaluations: 3
# Evolution settings
# -------------------------------------------------------
diff_based_evolution: true
max_code_length: 20000I also tried with qwen3:32 and qwen2-math:72b, which are bigger LLMs, on another machine, but I'm getting the same issue.
Logs 🗒️
You can find my logs here. Note that I'm getting lots of warning of the type:
WARNING - Iteration 25 error: No valid diffs found in response
⚠️ Update ⚠️
I tried gemini-2.5-flash and openai/gpt-oss-20b:free via openrouter.ai and both worked fine, so either the LLMs I used aren't good enough, or I haven't set up ollama properly, or there is a bug somewhere.