Welcome to the 14th International Conference on Evolutionary Multi-Criterion Optimization, hosted by the University of Exeter in the beautiful city of Exeter, United Kingdom.
About EMO 2027
The International Conference on Evolutionary Multi-Criterion Optimization (EMO) is the premier venue for researchers and practitioners working on multi-objective and many-objective optimization using evolutionary and nature-inspired computation approaches.
EMO 2027 will bring together leading experts from academia and industry to present and discuss the latest advances in evolutionary multi-objective optimization, including theoretical foundations, algorithm design, performance assessment, and real-world applications.
Conference Format
EMO 2027 will feature:
- 1 Tutorial Day: Monday, 5 April - In-depth tutorials on cutting-edge topics
- 3 Scientific Days: Tuesday-Thursday, 6-8 April - Keynotes and single-track sessions across EMO, MCDM, and Industry topics
- Social Events: Welcome reception, boat tour, and conference banquet
- Outstanding EMO Paper Awards: Recognition of outstanding contributions
Papers
Peer-reviewed research presentations from the EMO community
Keynotes
Inspiring talks from world-leading researchers
Tutorials
Hands-on learning from leading experts
Networking
Connect with peers at social events
Important Dates
All deadlines are at 23:59 Anywhere on Earth
Paper Submission Deadline
September 28, 2026Author Notification
November 30, 2026Camera-Ready Deadline
December 14, 2026Early Registration Deadline
TBCRegular Registration Deadline
TBCConference Begins
April 05, 2027Venue
EMO 2027 will be held at the XFi Building on the University of Exeter’s picturesque Streatham Campus. The venue features modern conference facilities including the 174-seat Henderson Lecture Theatre and the XFi Break-out Area for posters and networking.
Topics of Interest
We welcome submissions on all aspects of multi-objective optimization, including but not limited to:
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Algorithms, Theory, and Methods: Evolutionary and population-based algorithms, Surrogate-based or Bayesian optimization methods, Machine learning/artificial intelligence-based hybrid methods, Theoretical foundations of multi-objective optimization, Preference-based and interactive methods
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Assessment, Decision-Making, and Applications: Multiple-criteria decision-making, Benchmarking, performance indicators, and visualization tools, Practical applications
Sponsors & Partners
Interested in sponsoring EMO 2027? Contact us for sponsorship opportunities.
Become a Sponsor