We propose a representation of optimization problem instances using a
transformer-based neural network architecture trained for the task of problem
classification of the 24 problem classes from the Black-box Optimization
Benchmarking (BBOB) benchmark. We show that transformer-based methods can be
trained to recognize problem classes with accuracies in the range of 70\%-80\%
for different problem dimensions, suggesting the possible application of
transformer architectures in acquiring representations for black-box
optimization problems.

我们提出了一种使用基于 Transformer 的神经网络架构对优化问题实例进行表示的方法，并对 Black-box Optimization Benchmarking（BBOB）基准测试中的 24 个问题类别进行了问题分类的训练。我们表明，基于 Transformer 的方法可以以 70％-80％的准确率识别不同问题维度的问题类别，这表明了在获取黑盒优化问题的表示方面应用 Transformer 架构的可能性。

TransOpt：用于优化问题分类的基于 Transformer 的表示学习

TransOpt: Transformer-based Representation Learning for Optimization  Problem Classification

A hypergraph is a data structure composed of nodes and hyperedges, where each
hyperedge is an any-sized subset of nodes. Due to the flexibility in hyperedge
size, hypergraphs represent group interactions (e.g., co-authorship by more
than two authors) more naturally and accurately than ordinary graphs.
Interestingly, many real-world systems modeled as hypergraphs contain
edge-dependent node labels, i.e., node labels that vary depending on
hyperedges. For example, on co-authorship datasets, the same author (i.e., a
node) can be the primary author in a paper (i.e., a hyperedge) but the
corresponding author in another paper (i.e., another hyperedge).
In this work, we introduce a classification of edge-dependent node labels as
a new problem. This problem can be used as a benchmark task for hypergraph
neural networks, which recently have attracted great attention, and also the
usefulness of edge-dependent node labels has been verified in various
applications. To tackle this problem, we propose WHATsNet, a novel hypergraph
neural network that represents the same node differently depending on the
hyperedges it participates in by reflecting its varying importance in the
hyperedges. To this end, WHATsNet models the relations between nodes within
each hyperedge, using their relative centrality as positional encodings. In our
experiments, we demonstrate that WHATsNet significantly and consistently
outperforms ten competitors on six real-world hypergraphs, and we also show
successful applications of WHATsNet to (a) ranking aggregation, (b) node
clustering, and (c) product return prediction.

本文提出了一个新问题，分类了边依赖节点标签。为了解决这个问题，我们提出了一种新的超图神经网络 WHATsNet，它可以根据超边的不同，反映参与节点的不同重要性，并在多个任务中显示其优越性。

超图节点边缘依赖标签的分类

Classification of Edge-dependent Labels of Nodes in Hypergraphs

IT helpdesks are charged with the task of responding quickly to user queries.
To give the user confidence that their query matters, the helpdesk will
auto-reply to the user with confirmation that their query has been received and
logged. This auto-reply may include generic `boiler-plate' text that addresses
common problems of the day, with relevant information and links. The approach
explored here is to tailor the content of the auto-reply to the user's problem,
so as to increase the relevance of the information included. Problem
classification is achieved by training a neural network on a suitable corpus of
IT helpdesk email data. While this is no substitute for follow-up by helpdesk
agents, the aim is that this system will provide a practical stop-gap.

文章探讨了使用神经网络分类 IT 帮助台邮件数据的方式，定制带有用户问题的自动回复内容，以提高其相关性，从而提供实用的临时解决方案。

面向用户帮助台自动回复的问题分类

Problem Classification for Tailored Helpdesk Auto-Replies

In this paper, we explore the theory and expand upon the practice of fitness
landscape analysis for optimization problems over the space of permutations.
Many of the computational and analytical tools for fitness landscape analysis,
such as fitness distance correlation, require identifying a distance metric for
measuring the similarity of different solutions to the problem. We begin with a
survey of the available distance metrics for permutations, and then use
principal component analysis to classify these metrics. The result of this
analysis aligns with existing classifications of permutation problem types
produced through less formal means, including the A-permutation, R-permutation,
and P-permutation types, which classifies problems by whether absolute position
of permutation elements, relative positions of elements, or general precedence
of pairs of elements, is the dominant influence over solution fitness.
Additionally, the formal analysis identifies subtypes within these problem
categories. We see that the classification can assist in identifying
appropriate metrics based on optimization problem feature for use in fitness
landscape analysis. Using optimization problems of each class, we also
demonstrate how the classification scheme can subsequently inform the choice of
mutation operator within an evolutionary algorithm. From this, we present a
classification of a variety of mutation operators as a counterpart to that of
the metrics. Our implementations of the permutation metrics, permutation
mutation operators, and associated evolutionary algorithm, are available in a
pair of open source Java libraries. All of the code necessary to recreate our
analysis and experimental results are also available as open source.

本文研究了适应性函数分析在排列优化问题上的理论与实践，着重探讨了距离度量问题及其分类方法，并利用主成分分析法对于度量方法进行分类，揭示出排列优化问题的分类规律及其子类型，利用其进行突变算子的选择和进化算法的优化。