United Kingdom

In this talk, some research topics that are worth exploring (from the personal perspective of the speaker) in evolutionary multi-objective optimization will be briefly discussed. Such topics include scalability of multi-objective evolutionary algorithms (both in objective and in decision variable space), indicator-based selection, hyper-heuristics, parallelism and scalarizing functions. In the final part of the talk, some personal thoughts about the future of the field will also be briefly discussed.

IJCCI 2021

Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal Perspective

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation. 

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

keynote

One of the main assumptions of classical machine learning is that data are generated by a stationary concept. This is often violated in practice if unexpected events happen, sensor drift is present, of models should be individualized for a specific user, for example. In such situations, a number of challenges occur: How to detect that drift occurs? How to adapt the model on the fly, given new data, without catastrophic forgetting? How to incorporate measures which can mediate suboptimal behavior? In the talk, we will address three algorithmic challenges: flexible drift detection with minimum requirements, life-long model adaptation, and reject options. We will demonstrate the behavior of the algorithmic models in the context of medical applications and industry4.0, respectively.

Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal Perspective

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