| |
| """Function minimization example for OpenEvolve""" |
| import numpy as np |
|
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|
|
| 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) |
| """ |
| |
| 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): |
| |
| 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 |
|
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| |
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|
| |
| def evaluate_function(x, y): |
| """The complex function we're trying to minimize""" |
| return np.sin(x) * np.cos(y) + np.sin(x * y) + (x**2 + y**2) / 20 |
|
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|
|
| def run_search(): |
| x, y, value = search_algorithm() |
| return x, y, value |
|
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|
|
| if __name__ == "__main__": |
| x, y, value = run_search() |
| print(f"Found minimum at ({x}, {y}) with value {value}") |
|
|
