# 初始程序（随机搜索）
def minimize_function(func, bounds, max_evals=1000):
    best_x, best_val = None, float('inf')
    for _ in range(max_evals):
        x = random_point_in_bounds(bounds)
        val = func(x)
        if val < best_val:
            best_x, best_val = x, val
    return best_x, best_val

# 进化程序（模拟退火 + 自适应冷却）
def minimize_function(func, bounds, max_evals=1000):
    x = random_point_in_bounds(bounds)
    temp = adaptive_initial_temperature(func, bounds)
    
    for i in range(max_evals):
        neighbor = generate_neighbor(x, temp, bounds)
        delta = func(neighbor) - func(x)
        
        if delta < 0 or random.random() < exp(-delta/temp):
            x = neighbor
            
        temp *= adaptive_cooling_rate(i, max_evals)  # 动态冷却
    
    return x, func(x)

# 评估器可以返回执行上下文
from llm_evolve.evaluation_result import EvaluationResult

return EvaluationResult(
    metrics={"performance": 0.85, "correctness": 1.0},
    artifacts={
        "stderr": "警告：次优内存访问模式",
        "profiling_data": {...},
        "llm_feedback": "代码正确但可以使用更好的变量名",
        "build_warnings": ["未使用变量x"]
    }
)