Scope and aim

The by far most common approach towards understanding and developing optimization heuristics are analyses of the algorithms in the performance space. Some researchers also consider behavior in the decision space. It is well understood, however, that the interplay between decision and performance space is the most critical. Rigorously analyzing these relationships, however, is a tedious and complex task. A key technique developed to support such analyses is fitness landscape analysis (FLA). FLA aims to characterize properties of an optimization problem through sets of features, which measure different characteristics such as its degree of separability, its multimodality, etc.

Per-instance automated algorithm selection and configuration techniques use FLA to train meta-models which aim to predict which algorithm or which configuration works well on a given problem instance. FLA-based per-instance selection and configuration have shown promising performances for a number of classical optimization problems, including SAT solving, AI planning, etc.

In the context of black-box optimization, FLA requires the approximation of feature values through a number of samples. Key design questions in this context concern the selection of meaningful features, their efficient computation, the number of samples required to obtain reliable approximations, the distribution of these samples, the possibility to use algorithms’ trajectory data for feature computation, and many more. Research addressing these questions is subsumed under the term “exploratory landscape analysis” (ELA). In ELA, a large number of different features have been proposed, which raise up the need of feature selection, since many features can be highly correlated and have a decremental impact on understanding of the underlying recommendations. This is where representation learning comes into play. Representation learning has its most important applications in machine learning, where bias and redundancies in data can have severe effects on performance. It focuses on methods that automatically learn new data representations (i.e., feature engineering) using the raw data needed to improve the performance of machine learning tasks. Representation learning methods are also successfully used to reduce the dimension of the data, via automatically detecting correlations.

In this special session, we are particularly interested in studying how representation learning can contribute to improved performance and to a better understanding of ELA-based analyses, e.g., by automatically reducing bias, correlations and redundancies in the feature data.




Topics of Interest

  • Representation learning techniques for structured, unstructured, and graph data
  • Exploratory landscape analysis (ELA) for feature engineering of the landscape space
  • Feature selection, ranking and sensitivity analysis
  • Sensitivity analysis of sampling techniques applied in ELA
  • Representation learning applied on landscape data
  • Representation learning applied on performance data
  • Improving understanding of data (landscape and/or performance) through visualization techniques
  • Landscape data representation in automatic algorithm selection and configuration
  • Performance data representation in automatic algorithm selection and configuration
  • Machine learning for automatic algorithm selection and configuration
  • Meta-learning
  • Transfer of approaches between machine learning and optimization
  • Taxonomies/ontologies for describing the algorithm instance space
  • Complementary analysis of different benchmarking datasets




Submission

    All submissions should follow the CEC2021 submission guidelines provided at IEEE CEC 2021 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on RepL4Opt: Representation Learning meets Meta-heuristic Optimization. All papers accepted and presented at CEC 2021 will be included in the conference proceedings published by IEEE Explore.

    In order to participate to this special session, full or student registration of CEC 2021 is needed.




Important dates

  • Paper submission: 21 February 2021
  • Paper acceptance notification: 6 April 2021
  • Final paper submission: 23 April 2021



Organizers

Tome Eftimov

Computer Systems Depratment

Jožef Stefan Institue, Slovenia

Carola Doerr

CNRS, LIP6

Sorbonne University, France

Peter Korošec

Computer Systems Depratment

Jožef Stefan Institue, Slovenia




Aldeida Aleti - Monash University, Australia
Gjorgjina Cenikj - Jožef Stefan Institute, Slovenia
Nguyen Dang - University of St Andrews, United Kingdom
Bilel Derbel - University of Lille, Inria, France
Johann Dreo -
Andries Engelbrecht - Stellenbosch University, South Africa
Martin Holena - Academy of Sciences of the Czech Republic
Laetitia Jourdan - University of Lille, France
Pascal Kerschke -
Dragi Kocev - Jožef Stefan Institute, Slovenia
Arnaud Liefooghe - University of Lille, France
Marius Lindauer - Leibniz University Hannover, Germany
Manuel López-Ibáñez - The University of Manchester, United Kingdom
Katherine Malan - University of South Africa, South Africa
Mustafa Misir - Istinye University, Turkey
Mario Andrés Muñoz - The University of Melbourne, Australia
Gorjan Popovski - Jožef Stefan Institute, Slovenia
Mike Preuss - Leiden University, Netherlands
Quentin Renau - Thales and École Polytechnique, France
Thomas Stützle - Université libre de Bruxelles, Brussels
Urban Škvorc - Jožef Stefan Institute, Slovenia
Eva Tuba - Singidunum University, Republic of Serbia
Koen van der Blom - Leiden University, Netherlands
Bas van Stein - Leiden University, Netherlands
Ivona Vasileska - University of Ljubljana, Slovenia
Sebastien Verel - Univ. of the Littoral Opal Coast, France
Diederick Vermetten - Ledien University, Netherlands
Markus Wagner - University of Adelaide, Australia
Hao Wang - Leiden University, Netherlands
Marcel Wever - Paderborn University, Germany
Thomas Weise - Hefei University, China
Bing Xue - Victoria University of Wellington, New Zeland
Aleš Zamuda - University of Maribor, Slovenia