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How to select a winner in evolutionary optimization?

机译：如何在进化优化中选择赢家？

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摘要

In many evolutionary optimization domains evaluations are noisy. The candidates are tested on a number of randomly drawn samples, such as different games played, different physical simulations, or different user interactions. As a result, selecting the winner is a multiple hypothesis problem: The candidate that evaluated the best most likely received a lucky selection of samples, and will not perform as well in the future. This paper proposes a technique for selecting the winner and estimating its true performance based on the smoothness assumption: Candidates that are similar perform similarly. Estimated fitness is replaced by the average fitness of candidate's neighbors, making the selection and estimation more reliable. Simulated experiments in the multiplexer domain show that this technique is reliable, making it likely that the true winner is selected and its future performance is accurately estimated.
机译：在许多进化优化领域中，评估都是嘈杂的。在大量随机抽取的样本上测试候选者，例如不同的游戏，不同的物理模拟或不同的用户交互。结果，选择获胜者是一个多重假设问题：评估最佳可能性的候选人获得了幸运的样本选择，并且将来的表现将不那么好。本文提出了一种基于平滑度假设来选择获胜者并估算其真实表现的技术：相似的候选人表现相似。估计的适应度将替换为候选邻居的平均适应度，从而使选择和估计更加可靠。在多路复用器域中进行的仿真实验表明，该技术是可靠的，从而有可能选择了真正的赢家并准确估计了其未来性能。

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