Knowledge Representation in Transfer Optimisation System and Applications for Highly Configurable Software Systems

传输优化系统中的知识表示及高度可配置软件系统的应用

• 批准号: 2404317
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2404317
• 关键词: Knowledge; Representation; Transfer; Optimisation; System

This project plan to develop transfer optimisation algorithms that combines the idea between nature-inspired optimisation and transfer learning to equip an optimisation algorithm with adequate intelligence thus lead to a self-adaptive behaviour. To this end, the research will focus on one of the key questions to the endeavour of transfer optimisation, i.e., the knowledge representation and metrics used to evaluate and compare the similarity between different "experience" learned from the previous optimisation process. By doing so, it is able to overcome the negative transfer which brings disasters to transfer irrelevant or useless knowledge across tasks.We will start from graph theory given that graph is a general but powerful representation to various structures. In this case, we envisage that it is able to be the building block for knowledge representation for various landscapes. Representation learning techniques, which learn the intrinsic structure and representation of the data to facilitate useful information extraction, will be developed to understand the problem features from the representation of the fitness landscape itself. As for continuous variables, I will study to use reconstruction-based approaches that learns a parametric mapping from observed data to a representation like the autoencoder framework. For discrete variables, I will study to represent the fitness landscape as an information network. Then network representation learning approaches will be developed to learn a latent low-dimensional representations of network vertices while preserving network topology structure. In order to measure the similarity between different knowledge, I will develop some metrics to serve the quantitative evaluation. This is essentially related to the way how the knowledge is represented.For the knowledge represented as a low-dimensional encoder, I will evaluate similarity based on standard distance measures like Euclidean distance. For the knowledge represented as an information network, I will study from the graph matching perspective [2] and to develop similarity functions to measure the structural similarity between different networks.Once the knowledge representation and similarity measure are developed. I will study how to use them within nature-inspired computation to come up with a transfer optimisation algorithm.Many transfer learning techniques in the machine learning literature [5] are able to serve the purpose of transfer learning. In particular, I will consider two levels of knowledge transfer. One is genetic-level which aims to leverage the optima found in the previous optimisation exercises to accelerate the underlying optimisation. The other one is model level which is going to use transfer learning techniques to align the models across various tasks.The transfer optimisation algorithms developed in this project will be applied to optimise the non-functional performance of highly configurable software systems. Modern industrial software systems are super complex with many configuration options, the setting of which is directly related to their non-functional performance. It is arguable that those systems are too complex to be manually configured in order to achieve their peak performance at runtime under various environments and different user requirements. It is also time consuming to evaluate the non-functional performance of the underlying system when it incurs the throughput of huge volume of data. Building a surrogate to understand and predict the effect of a configuration option is promising alternative to enable the optimisation of a self-adaptive software system at runtime. More specifically, the knowledge representation developed in this PhD project will serve the purpose of surrogate modelling whilst the transfer optimisation will be used to learn and accumulate knowledge through optimisation.

该项目计划开发转移优化算法，将自然启发的优化和转移学习之间的想法结合起来，以配备具有足够智能的优化算法，从而导致自适应行为。为此，研究将重点关注转移优化的努力的关键问题之一，即，用于评估和比较从先前优化过程中汲取的不同“体验”之间的相似性，用于评估和比较了知识表示和指标。通过这样做，它能够克服负面转移，从而使灾难转移到整个任务中无关或无用的知识。我们将从图理论开始，因为图是对各种结构的一般但有力的表示。在这种情况下，我们设想它能够成为各种景观知识表示的基础。将开发代表学习技术，这些技术学习了数据的内在结构和数据的表示，以促进有用的信息提取，以了解来自健身景观本身的代表的问题特征。至于连续变量，我将研究使用基于重建的方法，该方法从观察到的数据到诸如自动编码器框架之类的表示形式学习参数映射。对于离散变量，我将研究以表示健身景观作为信息网络。然后，将开发网络表示学习方法，以学习网络顶点的潜在低维表示，同时保留网络拓扑结构。为了衡量不同知识之间的相似性，我将开发一些指标来服务定量评估。这基本上与知识的表示方式有关。对于表示为低维编码器的知识，我将根据欧几里得距离等标准距离衡量标准评估相似性。对于表示为信息网络的知识，我将从图形匹配的角度[2]研究并开发相似性函数以衡量不同网络之间的结构相似性。开发知识表示和相似性度量。我将研究如何在自然风格的计算中使用它们来提出转移优化算法。机器学习文献中的Many转移学习技术[5]能够达到转移学习的目的。特别是，我将考虑两个知识转移级别。一个是遗传水平，旨在利用先前优化练习中发现的最佳功率来加速基础优化。另一个是模型级别，它将使用传输学习技术在各种任务上对齐模型。该项目中开发的转移优化算法将用于优化高度可配置的软件系统的非功能性能。现代工业软件系统非常复杂，具有许多配置选项，其设置与其非功能性能直接相关。可以说，这些系统太复杂而无法手动配置，以至于在各种环境和不同的用户需求下在运行时实现其峰值性能。当基础系统产生大量数据的吞吐量时，评估基础系统的非功能性能也很耗时。建立一个替代物来理解和预测配置选项的效果是有希望的替代方法，可以在运行时优化自适应软件系统。更具体地说，该博士学位项目中开发的知识表示形式将达到替代建模的目的，而转移优化将用于通过优化学习和积累知识。