DTEO@GECCO

6th GECCO Workshop on Decomposition Techniques in Evolutionary Optimization (DTEO)

July 14 - 18, 2025, Malaga, Spain

Held in conjunction with the ACM Genetic and Evolutionary Computation Conference (GECCO 2025)

Past DTEO editions : @GECCO'22 (Boston); @GECCO'21 (Lille); @GECCO'20 (Cancun); @GECCO'19 (Prague); @GECCO'18 (Japan)

Overview and Scope

Tackling an optimization problem using decomposition consists in transforming (or re-modeling or re-thinking) it into multiple, a priori smaller and easier, problems that can be solved cooperatively. A number of techniques are being actively developed by the optimization and evolutionary computing community in order to explicitly or implicitly design decomposition with respect to four facets of an optimization problem: (i) the environmental parameters, (ii) the decision variables, (iii) the objective functions, and (iv) the available computing resources. The workshop aims to be a unified opportunity to report the recent advances in the design, analysis and understanding decomposition techniques and to discuss the current and future challenges in applying decomposition to the increasingly big and complex nature of optimization problems (e.g., large number of variables, large number of objectives, multi-modal problems, simulation optimization, uncertain scenario-based optimization) and its suitability to modern large scale compute environments (e.g., massively parallel and decentralized algorithms, large scale divide-and-conquer parallel algorithms, expensive optimization).

Topics of interest

The workshop focus is on (but not limited to) the developmental, implementational, theoretical and applied aspects of:

Large scale decomposition, e.g., decomposition in decision space, gray-box optimization, co-evolutionary algorithms, grouping and cooperative techniques, decomposition for constraint handling
Multi- and Many- objective decomposition, e.g., aggregation and scalarizing approaches, cooperative and hybrid island-based design, (sub-)population decomposition and mapping
Parallel and distributed evolutionary decomposition, e.g., scalability with respect to decision and objective spaces, divide-and-conquer decentralized techniques, distribution of compute efforts, scalable deployments on heterogeneous and massively parallel compute environments
General-purpose decomposition related techniques, e.g., machine-learning and model assisted decomposition, offline and on-line configuration of decomposition, search-region decomposition and multiple surrogates, parallel expensive optimization
Understanding and benchmarking decomposition techniques

Submissions

We invite submissions of the following types of papers:

research papers (up to 8 pages)
position papers (up to 2 pages)

Accepted submissions will be presented during the workshop and will appear in the GECCO Companion ACM proceedings. Paper's format should follow the GECCO 2025 instructions.

Submissions of early and in-progress work are encouraged. Authors of accepted papers proposing novel software developments will be encouraged to give a demo or a short introductory tutorial. Authors of accepted papers describing novel software or technical developments will be encouraged to give a demonstration during the workshop.