Keep Learning

保持学习

• 批准号: EP/V026534/1
• 负责人: Emma Hart
• 金额: $ 49.47万
• 依托单位: Edinburgh Napier University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV026534%2F1
• 关键词: Keep; Learning

Combinatorial problems are ubiquitous across many sectors in today's world: delivering optimised solutions can lead to considerable economic benefits in many fields such as logistics, packing, design and scheduling (of either people or processes). In a typical scenario, instances (for example, a set of goods to deliver) arrive frequently in a continual stream and a solution needs to be quickly produced. Although there are many well-known approaches to developing optimisation algorithms, most suffer from a problem that is now becoming apparent across the breadth of Artificial Intelligence: systems are limited to performing well on data that is similar to that encountered in their design process, and are unable to adapt when encountering situations outside of their original programming.For real-world optimisation this is particularly problematic. If optimisers are trained in a one-off process then deployed, the system remains static, despite the fact that optimisation occurs in a dynamic world of changing instance characteristics, changing user-requirements and changes in operating environments that influence solution quality (e.g. breakdowns in a factory or traffic in a city). Such changes may be either gradual, or sudden. In the best case this leads to systems that deliver sub-optimal performance, while at worst, systems that are completely unfit for purpose. Moreover, a system that does not adapt wastes an obvious opportunity to improve its own performance over time as it solves more and more instances.The targeted breakthrough of this proposal is to develop a dynamic optimisation system that continually adapts its operating mechanism and its algorithms over time to remain fit-for-purpose - a radical switch from the current one-off design and deployment approach to design of optimisers. The system will:- Go beyond simply being reactive to being proactive in that it will predict the nature of upcoming instances and speculate about potential future scenarios. In response to these predictions, it will autonomously pre-generate and/or reconfigure suitable algorithms, followed by creation of appropriate mappings from instance to solver, in order to pre-prepare for these future scenarios. It will also respond to user requests to generate instances with specific characteristics and solvers to match them, based on the user's in-depth knowledge of their own business and sector.- Autonomously improve its own behaviour over time, continually updating its algorithms and methods as it learns from its experience of solving more and more instances.- Support optimisation with respect to multiple user objectives and requirements via its use of a diverse portfolios of algorithms, that range from those which generate acceptable solutions in a very short time to those that have long running time but deliver the highest possible quality.To succeed we will make novel advances in building proactive, continually self-adapting systems and in optimisation/algorithm-selection, enhanced by integration with the latest tools from machine-learning. Benefits will be realised by any business that attempts to optimise their processes in dynamic environments, in which customer demands vary, business requirements change, and the operating environment is subject to unexpected changes. Relevant application domains include (but are not limited to) workforce scheduling, logistics and infrastructure design

组合问题在当今世界的许多行业中无处不在：提供优化的解决方案可以在许多领域带来可观的经济效益，如物流、包装、设计和调度(无论是人员还是流程)。在典型场景中，实例(例如，要交付的一组商品)频繁到达，需要快速生成解决方案。尽管有许多开发优化算法的众所周知的方法，但大多数方法都受到一个问题的困扰，这个问题在人工智能的各个领域都变得越来越明显：系统仅限于在与设计过程中遇到的数据相似的数据上表现良好，并且当遇到原始程序之外的情况时无法适应。对于现实世界的优化来说，这是一个特别有问题的问题。如果优化器在一次性流程中接受培训，然后部署，系统将保持静态，尽管优化发生在一个动态世界中，其中包括不断变化的实例特征、不断变化的用户要求和影响解决方案质量的操作环境变化(例如，工厂故障或城市交通故障)。这些变化可能是渐进的，也可能是突然的。在最好的情况下，这会导致系统性能不佳，而在最坏的情况下，系统完全不适合使用。此外，不适应的系统浪费了一个随着时间的推移而提高自身性能的明显机会，因为它解决了越来越多的实例。这一提议的目标突破是开发一种动态优化系统，该系统随着时间的推移不断调整其操作机制和算法，以保持适合于目的-从当前的一次性设计和部署方法到优化器设计的根本转变。该系统将：-超越简单的主动反应，因为它将预测即将到来的情况的性质，并推测潜在的未来情景。作为对这些预测的响应，它将自主地预先生成和/或重新配置合适的算法，然后创建从实例到解算器的适当映射，以便为这些未来的场景预先做好准备。它还将响应用户的请求，根据用户对自己业务和部门的深入了解，生成具有特定特征的实例和解算器，以匹配它们。-随着时间的推移，自主改进自己的行为，随着从解决越来越多实例的经验中学习，不断更新算法和方法。-通过使用不同的算法组合，支持针对多个用户目标和要求进行优化，这些算法包括在非常短的时间内生成可接受的解决方案的算法，以及那些运行时间较长但提供最高质量的算法。为了成功，我们将在构建主动、通过与机器学习的最新工具集成，不断地自适应系统和优化/算法选择。任何试图在客户需求变化、业务需求变化和运营环境发生意外变化的动态环境中优化其流程的企业都将实现收益。相关应用领域包括(但不限于)劳动力调度、物流和基础设施设计

项目成果

A Feature-Free Approach to Automated Algorithm Selection

一种无特征的自动算法选择方法

• DOI: 10.1145/3583133.3595832
• 发表时间: 2023
• 作者: Alissa M

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches

• DOI: 10.1007/s10732-022-09505-4
• 发表时间: 2022-03
• 期刊: Journal of Heuristics
• 影响因子: 2.7
• 作者: M. Alissa;Kevin Sim;E. Hart

Women in Computational Intelligence - Key Advances and Perspectives on Emerging Topics

计算智能领域的女性 - 新兴主题的主要进展和观点

• DOI: 10.1007/978-3-030-79092-9_9
• 发表时间: 2022
• 作者: Hart E

Applications of Evolutionary Computation - 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12-14, 2023, Proceedings

进化计算的应用 - 第 26 届欧洲会议，EvoApplications 2023，作为 EvoStar 2023 的一部分举行，捷克共和国布尔诺，2023 年 4 月 12-14 日，会议记录

• DOI: 10.1007/978-3-031-30229-9_22
• 发表时间: 2023
• 作者: Vermetten D

Learning-Based Neural Ant Colony Optimization

• DOI: 10.1145/3583131.3590483
• 发表时间: 2023-07
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Yi Liu;Jiang Qiu;E. Hart;Yilan Yu;Zhongxue Gan;Wei Li