We present the first nontrivial procedure for configuring heuristic
algorithms to maximize the utility provided to their end users while also
offering theoretical guarantees about performance. Existing procedures seek
configurations that minimize expected runtime. However, very recent theoretical
work argues that expected runtime minimization fails to capture algorithm
designers' preferences. Here we show that the utilitarian objective also
confers significant algorithmic benefits. Intuitively, this is because mean
runtime is dominated by extremely long runs even when they are incredibly rare;
indeed, even when an algorithm never gives rise to such long runs,
configuration procedures that provably minimize mean runtime must perform a
huge number of experiments to demonstrate this fact. In contrast, utility is
bounded and monotonically decreasing in runtime, allowing for meaningful
empirical bounds on a configuration's performance. This paper builds on this
idea to describe effective and theoretically sound configuration procedures. We
prove upper bounds on the runtime of these procedures that are similar to
theoretical lower bounds, while also demonstrating their performance
empirically.

我们提出了第一个非平凡的程序，用于配置启发式算法以最大化其为最终用户提供的效用，并同时提供关于性能的理论保证。我们展示了实用目标也带来了相当大的算法好处，在平均运行时间最小化无法捕捉到算法设计者的偏好的情况下表现出更好的性能。本文基于此思想描述了有效且具有理论基础的配置程序，并在理论下界类似的情况下证明了这些程序的运行时间上界，同时通过实验证明了它们的性能。