EMO 2019 : Evolutionary Multi-Criterion Optimization

  in Conferences   Posted on July 30, 2017

Conference Information

Submission Deadline Wednesday 24 Oct 2018
Conference & Submission Link https://www.coin-laboratory.com/emo2019
Conference Dates Mar 10, 2019 - Mar 13, 2019
Conference Address East Lansing, United States
Proceedings indexed by
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Google Scholar H5-index: 15
CORE 2017 Rating:
Guide2Research Overall Ranking: 317
Category Rankings
Computational Theory and Mathematics 42

Conference Call for Papers

10th International Conference on Evolutionary Multi-Criterion Optimization

March 10-13, 2019 in East Lansing, Michigan, USA

Submission Deadline: 24 September 2018

EMO 2019 is the 10th Edition of International Conference Series on Evolutionary Multi- Criterion Optimization (EMO), aiming to continue the success of previous EMO conferences. We will bring together both the EMO, multiple criteria decision-making (MCDM) communities, and other related fields and, moreover, focus on solving real-world problems in government, business and industry. Michigan is the birthplace of genetic algorithms research and EMO 2019 will plan on celebrating past 50 years of research and development in evolutionary computation and 10th edition of EMO conference series.

Following the tradition of the EMO community, the conference will be in single session format, with papers provided as either oral or as poster presentations. The conference format will include:

• Invited keynote speakers

• EMO track

• MCDM track

• Industry track

The conference will take place at Michigan State University’s Kellogg Hotel and Conference center at East Lansing in Michigan, USA.

We aim to discuss all aspects of EMO development and deployment, including (but not limited to):

EMO for handling of continuous, combinatorial and/or mixed-integer problems

Constraint handling approaches

Uncertainty and noise handling approaches

Many-objective optimization

Theoretical foundations

Preference handling techniques

Performance evaluation and metrics


Local search techniques

Hybrid approaches

Point and population-based hybrids

EMO and MCDM hybrids

Stopping criteria

Parallel EMO models and implementations

Test functions and benchmark problems

Algorithm selection approaches

Comparative studies

Metamodeling methods in EMO

EMO in Bilevel and generic hierarchical optimization

Knowledge-driven EMO

Large-scale optimization

Real-world applications

EMO algorithm implementation

General Chairs:

Kalyanmoy Deb and Erik D. Goodman (Michigan State University)

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