Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types

Sellmann M, Tierney K. Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types. In: Kotsireas IS, Pardalos PM, eds. Learning and Intelligent Optimization. 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020: 102-116

Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach

El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Kotsireas IS, Pardalos PM, eds. Learning and Intelligent Optimization. 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020: 216-232

Real-time algorithm configuration

This dissertation presents a number of contributions to the field of algorithm configur- ation. In particular, we present an extension to the algorithm configuration problem, real-time algorithm configuration, where configuration occurs online on a stream of instances, without the need for prior training, and problem solutions are returned in the shortest time possible. We propose a framework for solving the real-time algorithm configuration problem, ReACT. With ReACT we demonstrate that by using the parallel computing architectures, commonplace in many systems today, and a robust aggregate ranking system, configuration can occur without any impact on performance from the perspective of the user. This is achieved by means of a racing procedure. We show two concrete instantiations of the framework, and show them to be on a par with or even exceed the state-of-the-art in offline algorithm configuration using empirical evaluations on a range of combinatorial problems from the literature. We discuss, assess, and provide justification for each of the components used in our framework instantiations. Specifically, we show that the TrueSkill ranking system commonly used to rank players’ skill in multiplayer games can be used to accurately es- timate the quality of an algorithm’s configuration using only censored results from races between algorithm configurations. We confirm that the order that problem instances arrive in influences the configuration performance and that the optimal selection of configurations to participate in races is dependent on the distribution of the incoming in- stance stream. We outline how to maintain a pool of quality configurations by removing underperforming configurations, and techniques to generate replacement configurations with minimal computational overhead. Finally, we show that the configuration space can be reduced using feature selection techniques from the machine learning literature, and that doing so can provide a boost in configuration performance

Passenger-Centric Urban Air Mobility: Fairness Trade-Offs and Operational Efficiency

Urban Air Mobility (UAM) has the potential to revolutionize transportation. It will exploit the third dimension to help smooth ground traffic in densely populated areas. To be successful, it will require an integrated approach able to balance efficiency and safety while harnessing common resources and information. In this work we focus on future urban air-taxi services, and present the first methods and algorithms to efficiently operate air-taxi at scale. Our approach is twofold. First, we use a passenger-centric perspective which introduces traveling classes, and information sharing between transport modes to differentiate quality of services. This helps smooth multimodal journeys and increase passenger satisfaction. Second, we provide a flight routing and recharging solution which minimizes direct operational costs while preserving long term battery life through reduced energy-intense recharging. Our methods, which surpass the performance of a general state-of-the-art commercial solver, are also used to gain meaningful insights on the design space of the air-taxi problem, including solutions to hidden fairness issues.Comment: Submitted to Transportation Research Part C: Emerging Technologie

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

3D Information Technologies in Cultural Heritage Preservation and Popularisation

This Special Issue of the journal Applied Sciences presents recent advances and developments in the use of digital 3D technologies to protect and preserve cultural heritage. While most of the articles focus on aspects of 3D scanning, modeling, and presenting in VR of cultural heritage objects from buildings to small artifacts and clothing, part of the issue is devoted to 3D sound utilization in the cultural heritage field

Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web

The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

State of the Art and Future Perspectives in Smart and Sustainable Urban Development

This book contributes to the conceptual and practical knowledge pools in order to improve the research and practice on smart and sustainable urban development by presenting an informed understanding of the subject to scholars, policymakers, and practitioners. This book presents contributions—in the form of research articles, literature reviews, case reports, and short communications—offering insights into the smart and sustainable urban development by conducting in-depth conceptual debates, detailed case study descriptions, thorough empirical investigations, systematic literature reviews, or forecasting analyses. This way, the book forms a repository of relevant information, material, and knowledge to support research, policymaking, practice, and the transferability of experiences to address urbanization and other planetary challenges