A5031667150- Metaheuristic Optimization Algorithms Research
- Machine Learning and Algorithms
- Constraint Satisfaction and Optimization
- Advanced Multi-Objective Optimization Algorithms
- Machine Learning and Data Classification
- Advanced Bandit Algorithms Research
- Reinforcement Learning in Robotics
- Data Management and Algorithms
- Advanced Control Systems Optimization
- Rough Sets and Fuzzy Logic
- Auction Theory and Applications
- Optimization and Search Problems
- AI-based Problem Solving and Planning
- Geographic Information Systems Studies
- Green IT and Sustainability
- Evolutionary Algorithms and Applications
- Smart Cities and Technologies
- Multi-Criteria Decision Making
- Mobile Crowdsensing and Crowdsourcing
- Human Mobility and Location-Based Analysis
TU Dortmund University
2015-2016
University of Trento
2008-2013
Route choice in multimodal networks shows a considerable variation between different individuals as well the current situational context. Personalization of recommendation algorithms are already common many areas, e.g., online retail. However, most routing applications still provide shortest distance or travel-time routes only, neglecting individual preferences situation. Both aspects particular importance setting attractivity some transportation modes such biking crucially depends on...
This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover front multiobjective optimization problem. ALP casts identification into supervised machine task. enables an analytical model be built. The computational effort in generating information is reduced by strategy. In particular, learned from set informative training objective vectors. vectors are approximated Pareto-optimal obtained solving different scalarized problem instances. experimental...
Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable Development Goals (SDGs), which stated that taking urgent action to tackle climate and impacts was key The 2021 World Summit finished with calls for governments take tougher measures towards reducing their carbon footprints. However, it not obvious how can make practical implementations achieve this goal. One challenge achieving reduced footprint gaining awareness of energy exhaustive system or...
This paper introduces CLEO, a novel preference elicitation algorithm capable of recommending complex objects in hybrid domains, characterized by both discrete and continuous attributes constraints defined over them. The assumes minimal initial information, i.e., set catalog attributes, defines decisional features as logic formulae combining Boolean algebraic the attributes. (unknown) utility decision maker (DM) is modelled weighted combination features. CLEO iteratively alternates step,...