Scope and aim

Automating the design of new heuristics that are suitable for a given optimization problem instance is a core research objective in computational intelligence. It is a demanding task, as it requires a solid understanding of the complex interactions between the algorithm configuration space and the problem space. Many different techniques for the automated selection, configuration, and design of black-box optimization algorithms co-exist, in a yet very unorganized way across different sub-domains of evolutionary computation, machine learning, and operations research.

This special session intends to bring together researchers interested in the automated design of optimization algorithms for black-box optimization. No restriction will be made on the particular application domain, i.e, we are equally interested in combinatorial/discrete/numerical/mixed-integer optimization, noisy/non-noisy settings, static/dynamic problems, single-/multi-objective optimization, different performance metrics, etc. The unifying theme are the approaches that are used to automate algorithms’ design.

Topics of Interest

  • Supervised ML for automated algorithm selection, configuration, and design
  • Unsupervised ML for automated algorithm selection, configuration, and design
  • Explainable machine learning for automated algorithm selection, configuration, and design
  • Meta-learning
  • Transfer of approaches between machine learning and optimization
  • Taxonomies/ontologies for describing the algorithm instance space
  • Complementary analysis of different benchmarking datasets
  • Representation learning techniques for structured, unstructured, and graph data
  • Exploratory landscape analysis for characterizing problem or algorithm space
  • Feature selection, ranking and sensitivity analysis
  • Representation learning applied on landscape data
  • Representation learning applied on performance data
  • Improving understanding of data (landscape and/or performance) through visualization techniques


    All submissions should follow the CEC2023 submission guidelines provided at IEEE CEC 2023 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on AAD4EC: Automated Algorithm Design for Evolutionary Computation. All papers accepted and presented at CEC 2023 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 2023 is needed.

Important dates

  • Paper submission: 27 January 2023
  • Paper acceptance notification: 14 April 2023
  • Final paper submission: 29 April 2023


Tome Eftimov

Computer Systems Depratment

Jožef Stefan Institue, Slovenia

Manuel López-Ibáñez

University of Manchester, UK

Carola Doerr


Sorbonne University, France

Peter Korošec

Computer Systems Depratment

Jožef Stefan Institue, Slovenia