用神经进化寻找更鲁棒的局部搜索算法，提升黑箱问题求解效率。
Discovering new robust local search algorithms with neuro-evolution
- 用神经网络替代传统规则，根据邻域信息决策下一步搜索方向。
- 在NK景观问题上验证，算法在不同问题规模和崎岖度下均表现稳定。
- 适合对智能优化算法设计、自动化搜索策略感兴趣的读者。
本文提出一种新方法，旨在克服局部搜索算法中的现有挑战。目标是优化局部搜索中每轮迭代的邻域转移决策过程。为此，我们引入一个神经网络模型，其输入信息与传统局部搜索算法一致。本文作为EvoCOP2024工作的延伸，探索了多种信息表示方式，以实现算法在保持高效性的同时，对目标函数单调变换具有鲁棒性。通过围绕NK景观问题构建实验框架，可灵活调节问题规模与崎岖度，评估该方法的有效性。结果表明，该方法为新型局部搜索算法的涌现及黑箱问题求解能力的提升提供了可行路径。最新版本发表于《SN Computer Science》（Springer）。
原文摘要 · Abstract (English)
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).
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