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Natural Computing - Special Issue on Understanding Natural Computing Algorithm Behaviour


Artificial Intelligence

Understanding of optimization algorithm’s behavior is a vital part that is needed for quality progress in the field of stochastic optimization algorithms. Too often (new) algorithms are setup and tuned only focusing on achieving the desired optimization goal. While this might be effective and efficient in short term, in long term this is insufficient due to the fact that this needs to be repeated for every new problem that arises. Such approach provides only minor immediate gains, instead of contributing to the progress in research on optimization algorithms. To be able to overcome this deficiency, we need to establish new standards for understanding optimization algorithm behavior, which will provide understanding of the working principles behind the stochastic optimization algorithms. This includes theoretical and empirical research, which would lead to providing insight into answering questions such as (1) why does an algorithm work for some problems but does not work for others, (2) how to explore the fitness landscape to gain better understanding of the algorithm’s behavior, and (3) how to interconnect stochastic optimization and machine learning to improve the algorithm’s behavior on new unseen instances.
The focus of this workshop is to highlight theoretical and empirical research that investigate approaches needed to analyze stochastic optimization algorithms and performance assessment with regard to different criteria. The main goal is to bring the problem and importance of understanding optimization algorithms closer to researchers and to show them how and why this is important for future development in the optimization community. This will help researchers/users to transfer the gained knowledge from theory into the real world, or to find the algorithm that is best suited to the characteristics of a given real-world problem.
This special issue seeks to provide an opportunity for researchers to present their original contributions on understanding of optimization algorithm behavior, and any related issues. The special issue topics include (but are not limited to) the following:
- Data-driven approaches (machine learning/information theory/statistics) for assessing algorithm performance
- Vector embeddings of problem search space
- Meta-learning
- New advances in analysis and comparison of algorithms
- Operators influence on algorithm behavior
- Parameters influence on algorithm behavior
- Theoretical algorithm analysis
The submitted manuscripts must not have been published or simultaneously submitted elsewhere. For the submitted extended papers, an extension of at least 30% beyond that in the published proceedings is expected. Each submitted paper (extended or not) will receive thorough reviews and evaluation. Papers will be selected based mainly on their originality, scientific and technical quality of the contributions, organization and presentation and relevance to the special issue. The NACO’s submission system is already open for submissions. When submitting your manuscript please select the article type "S.I.: Understanding Evolutionary Algorithm Behaviour". Please submit your manuscript before February 28, 2020.
Once your manuscript is accepted, it will go into production, and will be published either in 2021.
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal's homepage at:
Inquiries, including questions about appropriate topics, may be sent electronically to the Guest Editors.
IMPORTANT DATES (Subject to change)
- Paper submission due: February 28, 2020
- First-round acceptance decision notification: May 31, 2020
- First revision submission due: July 31, 2020
- Second-round acceptance decision notification: October 31, 2020
- Second revision submission due: November 30, 2020
- Notification of final decision: January 31, 2021
- Target (tentative) publication date: 2021
Dr. Christian Blum (Guest Lead Editor)
Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC), Spain
Dr. Tome Eftimov
Department of Biomedical Data Sciences, Center for Population Health Sciences, Stanford Medicine, Stanford University, USA
Dr. Peter Korošec
Jožef Stefan Institute, Ljubljana, Slovenia