利用神经进化发现新的鲁棒局部搜索算法
Discovering new robust local search algorithms with neuro-evolution
AI总结：
本文提出用神经进化改进局部搜索算法的邻域决策过程，通过在 NK 景观问题上实验，发现对目标函数单调变换具有鲁棒性的新型高效局部搜索算法。
摘要：
本文探索了一种旨在克服局部搜索算法领域现有挑战的新方法。我们的目标是改进局部搜索算法内部的决策过程，以便在每次迭代中尽可能在邻域内做出最佳转移。为了改进这一过程，我们提出使用一个神经网络，该网络具有与传统局部搜索算法相同的输入信息。本文是 EvoCOP2024 上所报告工作的扩展，我们研究了表示这些信息的不同方式，以使算法尽可能高效，同时对问题目标函数的单调变换具有鲁棒性。为了评估该方法的效率，我们构建了一个以 NK 景观问题为核心的实验设置，从而可以灵活调整问题规模和崎岖度。该方法为涌现新的局部搜索算法以及提升其在黑盒问题上的求解能力提供了一条有前景的途径。本文的最新版本发表于期刊 SN Computer Science（Springer）。
英文摘要：
This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. Our aim is to improve the decision process that takes place within a local search algorithm so as to make the best possible transitions in the neighborhood at each iteration. To improve this process, we propose to use a neural network that has the same input information as conventional local search algorithms. In this paper, which is an extension of the work presented at EvoCOP2024, we investigate different ways of representing this information so as to make the algorithm as efficient as possible but also robust to monotonic transformations of the problem objective function. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. This approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems. The last version of this article is published in the journal SN Computer Science (Springer).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
