This paper presents a new genetic algorithm encoding representation to solve
the travelling salesman problem. To assess the performance of the proposed
chromosome structure, we compare it with state-of-the-art encoding
representations. For that purpose, we use 14 benchmarks of different sizes
taken from TSPLIB. Finally, after conducting the experimental study, we report
the obtained results and draw our conclusion.

本文提出了一种新的遗传算法编码表示方法，用于解决旅行家问题。通过在 TSPLIB 中使用 14 个不同大小的基准测试数据，将其与现有编码表示方法相比较，并在实验研究后得出结论。

旅行推销员问题的新节点移位编码表示

A new node-shift encoding representation for the travelling salesman  problem

Double-strand DNA breaks (DSBs) are a form of DNA damage that can cause
abnormal chromosomal rearrangements. Recent technologies based on
high-throughput experiments have obvious high costs and technical
challenges.Therefore, we design a graph neural network based method to predict
DSBs (GraphDSB), using DNA sequence features and chromosome structure
information. In order to improve the expression ability of the model, we
introduce Jumping Knowledge architecture and several effective structural
encoding methods. The contribution of structural information to the prediction
of DSBs is verified by the experiments on datasets from normal human epidermal
keratinocytes (NHEK) and chronic myeloid leukemia cell line (K562), and the
ablation studies further demonstrate the effectiveness of the designed
components in the proposed GraphDSB framework. Finally, we use GNNExplainer to
analyze the contribution of node features and topology to DSBs prediction, and
proved the high contribution of 5-mer DNA sequence features and two chromatin
interaction modes.

本研究设计了基于图神经网络的 GraphDSB 方法，使用了不同的有效结构编码方法和结构信息来预测双链 DNA 断裂，无高昂成本和技术挑战。在 NHEK 和 K562 数据集上，验证了结构信息对 DSBs 预测的贡献，并通过剥离实验进一步证明了所设计组件在 GraphDSB 框架中的有效性。最后，我们使用 GNNExplainer 来分析节点特征和拓扑对 DSBs 预测的贡献，并证明了 5-mer DNA 序列特征和两种染色质相互作用模式的高贡献性。