Section: Partnerships and Cooperations
International Initiatives
Inria International Labs
Other IIL projects
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Title: Frontiers in Massive Optimization and Computational Intelligence (MODO)
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International Partner (Institution - Laboratory - Researcher): Shinshu University, Japan
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See also: https://sites.google.com/view/lia-modo/
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Abstract: The aim of MODO is to federate French and Japanese researchers interested in the dimensionality, heterogeneity and expensive nature of massive optimization problems. The team receives a yearly support for international exchanges and shared manpower (joint PhD students).
Inria Associate Teams Not Involved in an Inria International Labs
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Title: Three-fold decomposition in multi-objective optimization (DMO)
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International Partner (Institution - Laboratory - Researcher): University of Exeter, UK
Inria International Partners
Informal International Partners
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School of Public Health and Preventive Medicine, Monash University, Australia (ranked 73th over 1000 in the Shangai international ranking).
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Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Maracanaú, Brazil.
Participation in Other International Programs
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Title: Evolutionary many-objective optimization: application to smart cities and engineering design
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International Partner (Institution - Laboratory - Researcher): CINVESTAV-IPN, Mexico
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Abstract: The project is co-funded by ECOS Nord, France and ANUIES, Mexico. It is focused on evolutionary many-objective optimization and its application to smart cities and engineering design.
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Title: Bridging the gap between exact methods and heuristics for multi-objective search (MOCO-Search)
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International Partner (Institution - Laboratory - Researcher): University of Coimbra and University of Lisbon, Portugal
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Abstract: This international project for scientific cooperation (PICS), funded by CNRS and FCT, aims to fill the gap between exact and heuristic methods for multi-objective optimization. The goal is to establish the link between the design principles of exact and heuristic methods, to identify features that make a problem more difficult to be solved by each method, and to improve their performance by hybridizing search strategies. Special emphasis is given to rigorous performance assessment, benchmarking, and general-purpose guidelines for the design of exact and heuristic multi-objective search.