Scope and aim

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.

Topics of Interest

  • 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

Invited Speakers

Associate Professor Rong Qu

Faculty of Science

University of Nottingham

Title: Towards a Better Understanding of Search Algorithms: A New Standard on Algorithm Design

Along with the recent successes of machine learning to numerous applications comes the next challenge of general AI optimisation algorithms. In research, intelligent search algorithms are often designed case by case for specific problems. The rich knowledge of algorithm design is scattered and often discarded, leading to huge waste. In practice, practitioners are faced with the barrier of extensive expertise required to design effective algorithms. There is a lack of standard in algorithm design to support a deeper understanding on and retain coherent knowledge of good behaviours of search algorithms. Such standard is key to develop effective algorithms in both research and practice efficiently.

This talk introduces a new standard on algorithm design based on algorithmic components, and discuss some recent results on automated algorithm design for combinatorial optimisation problems. With the new standard, the new algorithms automatically evolved can be modelled in a consistent form of algorithmic components configured in the best way. These configurations thus can potentially provide a way to gain better understanding of algorithm behaviours. The talk will also present research on the search spaces of hyper-heuristics. As one of the general search algorithms, hyper-heuristics can also be modelled using the new standard, findings on which provide insights for future research on the new standard addressing various optimisation problems.


Important dates

  • Paper submission (extended): 27 March 2019
  • Decision notification: 7 April 2019
  • Final submission: 15 April 2019


Tome Eftimov

Department of Biomedical Data Sciences, Center for Population Health Sciences, Stanford Medicine

Stanford University, USA

Peter Korošec

Computer Systems Depratment

Jožef Stefan Institue, Slovenia

Christian Blum

Artificial Intelligence Research Institute (IIIA)

Spanish National Research Council (CSIC), Spain

Chaoli Sun – Taiyuan University of Science and Technology
Mukesh Prasad – University of Technology Sydney
Masaya Nakata – The University of Electro-Communications
Efrén Mezura-Montes – University of Veracruz
Qi Chen – Victoria University of Wellington
Wei-Chang Yeh – National Tsing Hua University
George Panoutsos – University of Sheffield
Joao Soares – University of Alberta
Massimiliano Vasile – University of Strathclyde
Siddhartha Bhattacharyya – RCC Institute of Information Technology
Martin Lukac - Nazarbayev University
Maysam Orouskhani - Islamic Azad University
Rui Wang - University of Sheffield
Barbara Koroušić Seljak – Jožef Stefan Institute
Rong Qu – University of Nottingham
Rok Hribar – Jožef Stefan Institute
Gregor Papa – Jožef Stefan Institute
Jurij Šilc – Jožef Stefan Institute
El-Ghazali Talbi – University Lille
Nouredine Melab – University Lille
Aleš Zamuda - University of Maribor