posted by user: hiroyukisato || 494 views || tracked by 1 users: [display]

MIE 2024 : IEEE WCCI2024 - CEC2024 Special Session on Make It Easy! – Evolutionary Computation with Additional Objective Functions

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/gl.cc.uec.ac.jp/cec2024-mie
 
When Jun 30, 2024 - Jul 5, 2024
Where Yokohama, Japan
Submission Deadline Jan 29, 2024
Notification Due Mar 15, 2024
Final Version Due May 1, 2024
Categories    evolutionary computation   fitness landscape   multi-objectivization
 

Call For Papers

Organizers:
Shoichiro Tanaka (The University of Electro-Communications, Japan),
Keiki Takadama (The University of Electro-Communications, Japan),
Hiroyuki Sato (The University of Electro-Communications, Japan)

Contact email:
cec2024-mie@hs.hc.uec.ac.jp

Website:
https://sites.google.com/gl.cc.uec.ac.jp/cec2024-mie

Scope and Topics:
Multi-objectivization is a new optimization paradigm that reformulates single-objective or multi-objective
optimization problems into problems with more objective functions. Adding an objective function reduces
the number of local optima and/or develops plateaus of incomparable solutions in search space, i.e.,
changes the fitness landscape. From this advantage, multi-objectivization aims to obtain more diverse and
higher-quality solutions than optimizing the original problem. However, many important issues remain
unsolved in multi-objectivization, e.g., When should problems be multi-objectivized? What and how many
objective functions should be added? Why do the additional objective functions make the problem easier?
How does the additional objective function change the landscape? To find the answer to these questions,
this special session aims to bring researchers together to explore novel methods and discuss the future
direction from the viewpoint of evolutionary computation.

The topics of this special session include but are not limited to the following topics:
- Multi-objectivization methods for continuous or combinatorial optimization problems
- Multi-objectivization methods for machine learning, dynamic or constrained optimization problems (includes studies considering other solution evaluation indicators besides the objective function value, such as novelty and diversity)
- Adaptive and dynamic multi-objectivization methods
- Empirical or theoretical analysis of the effects of additional objective functions or changing solution evaluation indicators on the algorithm performance
- Analysis of changes in the fitness landscape by the additional objective function (includes single- and multi-objective landscape analysis)
- Case studies of multi-objectivization in real-world problems

Related Resources

DSAI 2024   2nd International Conference on Data Science and Artificial Intelligence
ESCI 2024   IEEE WCCI2024 - CEC2024 Special Session on Evolutionary computation and swarm intelligence for dynamical environments and multitasking problems: Let two different approaches meet
WEIP 2024   Workshop on Evolutionary Information Processing
AAAI-MAKE 2024   AAAI 2024 Spring Symposium on Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge
ICCTech 2025   4th International Conference on Computer Technologies
IEEE BigData 2024   2024 IEEE International Conference on Big Data
SwarmEvo 2024   Special Issue: Peak and Bad-Case Performance of Swarm and Evolutionary Optimization Algorithms
IEEE-Ei/Scopus-ACEPE 2024   2024 IEEE Asia Conference on Advances in Electrical and Power Engineering (ACEPE 2024) -Ei Compendex
IEEE-Ei/Scopus-SGGEA 2024   2024 Asia Conference on Smart Grid, Green Energy and Applications (SGGEA 2024) -EI Compendex
S&P 2025   The 45th IEEE Symposium on Security and Privacy