def regret(p):
    f_values = np.array([obj_funcs[i](s) for (i, s) in enumerate(solutions)])
    cost = (f_values - np.array([f((p @ solutions)) for f in obj_funcs]))
    cost = np.asarray((cost ** 2))
    return (100 * sum((prob * cost)))
def regret(p):
    f_values = np.array([obj_funcs[i](s) for (i, s) in enumerate(solutions)])
    cost = (f_values - np.array([f((p @ solutions)) for f in obj_funcs]))
    cost = np.asarray((cost ** 2))
    return (100 * sum((prob * cost)))