Merge pull request #6 from codelion/feature/update-example

codelion · web-flow · commit 10a4ad3e301f · 2025-05-19T14:40:51.000+08:00
Feature/update example
diff --git a/README.md b/README.md
@@ -76,6 +76,7 @@ When resuming from a checkpoint:
 - The system loads all previously evolved programs and their metrics
 - Checkpoint numbering continues from where it left off (e.g., if loaded from checkpoint_50, the next checkpoint will be checkpoint_60)
 - All evolution state is preserved (best programs, feature maps, archives, etc.)
+- Each checkpoint directory contains a copy of the best program at that point in time
 
 Example workflow with checkpoints:
 
@@ -91,6 +92,32 @@ python openevolve-run.py examples/function_minimization/initial_program.py \
   --checkpoint examples/function_minimization/openevolve_output/checkpoints/checkpoint_50 \
   --iterations 50
 ```
+
+### Comparing Results Across Checkpoints
+
+Each checkpoint directory contains the best program found up to that point, making it easy to compare solutions over time:
+
+```
+checkpoints/
+  checkpoint_10/
+    best_program.py         # Best program at iteration 10
+    best_program_info.json  # Metrics and details
+    programs/               # All programs evaluated so far
+    metadata.json           # Database state
+  checkpoint_20/
+    best_program.py         # Best program at iteration 20
+    ...
+```
+
+You can compare the evolution of solutions by examining the best programs at different checkpoints:
+
+```bash
+# Compare best programs at different checkpoints
+diff -u checkpoints/checkpoint_10/best_program.py checkpoints/checkpoint_20/best_program.py
+
+# Compare metrics
+cat checkpoints/checkpoint_*/best_program_info.json | grep -A 10 metrics
+```
 ### Docker
 
 You can also install and execute via Docker:
diff --git a/examples/function_minimization/README.md b/examples/function_minimization/README.md
@@ -0,0 +1,179 @@
+# Function Minimization Example
+
+This example demonstrates how OpenEvolve can discover sophisticated optimization algorithms starting from a simple implementation.
+
+## Problem Description
+
+The task is to minimize a complex non-convex function with multiple local minima:
+
+```python
+f(x, y) = sin(x) * cos(y) + sin(x*y) + (x^2 + y^2)/20
+```
+
+The global minimum is approximately at (-1.704, 0.678) with a value of -1.519.
+
+## Getting Started
+
+To run this example:
+
+```bash
+cd examples/function_minimization
+python ../../openevolve-run.py initial_program.py evaluator.py --config config.yaml
+```
+
+## Algorithm Evolution
+
+### Initial Algorithm (Random Search)
+
+The initial implementation was a simple random search that had no memory between iterations:
+
+```python
+def search_algorithm(iterations=1000, bounds=(-5, 5)):
+    """
+    A simple random search algorithm that often gets stuck in local minima.
+    
+    Args:
+        iterations: Number of iterations to run
+        bounds: Bounds for the search space (min, max)
+        
+    Returns:
+        Tuple of (best_x, best_y, best_value)
+    """
+    # Initialize with a random point
+    best_x = np.random.uniform(bounds[0], bounds[1])
+    best_y = np.random.uniform(bounds[0], bounds[1])
+    best_value = evaluate_function(best_x, best_y)
+    
+    for _ in range(iterations):
+        # Simple random search
+        x = np.random.uniform(bounds[0], bounds[1])
+        y = np.random.uniform(bounds[0], bounds[1])
+        value = evaluate_function(x, y)
+        
+        if value < best_value:
+            best_value = value
+            best_x, best_y = x, y
+    
+    return best_x, best_y, best_value
+```
+
+### Evolved Algorithm (Simulated Annealing)
+
+After running OpenEvolve, it discovered a simulated annealing algorithm with a completely different approach:
+
+```python
+def simulated_annealing(bounds=(-5, 5), iterations=1000, step_size=0.1, initial_temperature=100, cooling_rate=0.99):
+    """
+    Simulated Annealing algorithm for function minimization.
+    
+    Args:
+        bounds: Bounds for the search space (min, max)
+        iterations: Number of iterations to run
+        step_size: Step size for perturbing the solution
+        initial_temperature: Initial temperature for the simulated annealing process
+        cooling_rate: Cooling rate for the simulated annealing process
+        
+    Returns:
+        Tuple of (best_x, best_y, best_value)
+    """
+    # Initialize with a random point
+    best_x = np.random.uniform(bounds[0], bounds[1])
+    best_y = np.random.uniform(bounds[0], bounds[1])
+    best_value = evaluate_function(best_x, best_y)
+
+    current_x, current_y = best_x, best_y
+    current_value = best_value
+    temperature = initial_temperature
+
+    for _ in range(iterations):
+        # Perturb the current solution
+        new_x = current_x + np.random.uniform(-step_size, step_size)
+        new_y = current_y + np.random.uniform(-step_size, step_size)
+
+        # Ensure the new solution is within bounds
+        new_x = max(bounds[0], min(new_x, bounds[1]))
+        new_y = max(bounds[0], min(new_y, bounds[1]))
+
+        new_value = evaluate_function(new_x, new_y)
+
+        # Calculate the acceptance probability
+        if new_value < current_value:
+            current_x, current_y = new_x, new_y
+            current_value = new_value
+
+            if new_value < best_value:
+                best_x, best_y = new_x, new_y
+                best_value = new_value
+        else:
+            probability = np.exp((current_value - new_value) / temperature)
+            if np.random.rand() < probability:
+                current_x, current_y = new_x, new_y
+                current_value = new_value
+
+        # Cool down the temperature
+        temperature *= cooling_rate
+
+    return best_x, best_y, best_value
+```
+
+## Key Improvements
+
+Through evolutionary iterations, OpenEvolve discovered several key algorithmic concepts:
+
+1. **Local Search**: Instead of random sampling across the entire space, the evolved algorithm makes small perturbations to promising solutions:
+   ```python
+   new_x = current_x + np.random.uniform(-step_size, step_size)
+   new_y = current_y + np.random.uniform(-step_size, step_size)
+   ```
+
+2. **Temperature-based Acceptance**: The algorithm can escape local minima by occasionally accepting worse solutions:
+   ```python
+   probability = np.exp((current_value - new_value) / temperature)
+   if np.random.rand() < probability:
+       current_x, current_y = new_x, new_y
+       current_value = new_value
+   ```
+
+3. **Cooling Schedule**: The temperature gradually decreases, transitioning from exploration to exploitation:
+   ```python
+   temperature *= cooling_rate
+   ```
+
+4. **Parameter Introduction**: The system discovered the need for additional parameters to control the algorithm's behavior:
+   ```python
+   def simulated_annealing(bounds=(-5, 5), iterations=1000, step_size=0.1, initial_temperature=100, cooling_rate=0.99):
+   ```
+
+## Results
+
+The evolved algorithm shows substantial improvement in finding better solutions:
+
+| Metric | Value |
+|--------|-------|
+| Value Score | 0.677 |
+| Distance Score | 0.258 |
+| Reliability Score | 1.000 |
+| Overall Score | 0.917 |
+| Combined Score | 0.584 |
+
+The simulated annealing algorithm:
+- Achieves higher quality solutions (closer to the global minimum)
+- Has perfect reliability (100% success rate in completing runs)
+- Maintains a good balance between performance and reliability
+
+## How It Works
+
+This example demonstrates key features of OpenEvolve:
+
+- **Code Evolution**: Only the code inside the evolve blocks is modified
+- **Complete Algorithm Redesign**: The system transformed a random search into a completely different algorithm
+- **Automatic Discovery**: The system discovered simulated annealing without being explicitly programmed with knowledge of optimization algorithms
+- **Function Renaming**: The system even recognized that the algorithm should have a more descriptive name
+
+## Next Steps
+
+Try modifying the config.yaml file to:
+- Increase the number of iterations
+- Change the LLM model configuration
+- Adjust the evaluator settings to prioritize different metrics
+- Try a different objective function by modifying `evaluate_function()`
diff --git a/examples/function_minimization/evaluator.py b/examples/function_minimization/evaluator.py
@@ -55,9 +55,9 @@ def evaluate(program_path):
         Dictionary of metrics
     """
     # Known global minimum (approximate)
-    GLOBAL_MIN_X = -1.76
-    GLOBAL_MIN_Y = -1.03
-    GLOBAL_MIN_VALUE = -2.104
+    GLOBAL_MIN_X = -1.704
+    GLOBAL_MIN_Y = 0.678
+    GLOBAL_MIN_VALUE = -1.519
 
     try:
         # Load the program
@@ -216,9 +216,9 @@ def evaluate(program_path):
 def evaluate_stage1(program_path):
     """First stage evaluation with fewer trials"""
     # Known global minimum (approximate)
-    GLOBAL_MIN_X = float(-1.76)
-    GLOBAL_MIN_Y = float(-1.03)
-    GLOBAL_MIN_VALUE = float(-2.104)
+    GLOBAL_MIN_X = float(-1.704)
+    GLOBAL_MIN_Y = float(0.678)
+    GLOBAL_MIN_VALUE = float(-1.519)
 
     # Quick check to see if the program runs without errors
     try:
diff --git a/examples/function_minimization/initial_program.py b/examples/function_minimization/initial_program.py
@@ -49,4 +49,3 @@ def run_search():
 if __name__ == "__main__":
     x, y, value = run_search()
     print(f"Found minimum at ({x}, {y}) with value {value}")
-    # The global minimum is around (-1.76, -1.03) with value -2.104
diff --git a/openevolve/controller.py b/openevolve/controller.py
@@ -353,10 +353,50 @@ def _save_checkpoint(self, iteration: int) -> None:
         checkpoint_dir = os.path.join(self.output_dir, "checkpoints")
         os.makedirs(checkpoint_dir, exist_ok=True)
 
-        # Save the database
+        # Create specific checkpoint directory
         checkpoint_path = os.path.join(checkpoint_dir, f"checkpoint_{iteration}")
+        os.makedirs(checkpoint_path, exist_ok=True)
+
+        # Save the database
         self.database.save(checkpoint_path, iteration)
 
+        # Save the best program found so far
+        best_program = None
+        if self.database.best_program_id:
+            best_program = self.database.get(self.database.best_program_id)
+        else:
+            best_program = self.database.get_best_program()
+
+        if best_program:
+            # Save the best program at this checkpoint
+            best_program_path = os.path.join(checkpoint_path, f"best_program{self.file_extension}")
+            with open(best_program_path, "w") as f:
+                f.write(best_program.code)
+
+            # Save metrics
+            best_program_info_path = os.path.join(checkpoint_path, "best_program_info.json")
+            with open(best_program_info_path, "w") as f:
+                import json
+
+                json.dump(
+                    {
+                        "id": best_program.id,
+                        "generation": best_program.generation,
+                        "iteration": iteration,
+                        "metrics": best_program.metrics,
+                        "language": best_program.language,
+                        "timestamp": best_program.timestamp,
+                        "saved_at": time.time(),
+                    },
+                    f,
+                    indent=2,
+                )
+
+            logger.info(
+                f"Saved best program at checkpoint {iteration} with metrics: "
+                f"{', '.join(f'{name}={value:.4f}' for name, value in best_program.metrics.items())}"
+            )
+
         logger.info(f"Saved checkpoint at iteration {iteration} to {checkpoint_path}")
 
     def _save_best_program(self, program: Optional[Program] = None) -> None:
