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8 or 12 July 2020, Cancun, Mexico
Held in conjunction with the ACM Genetic and Evolutionary Computation Conference (GECCO 2020)
Past DTEO editions : @GECCO'19 (Prague); @GECCO'18 (Japan)
Overview and Scope
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
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, 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
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 2020 ACM 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.
Details on the submission procedure will be communicated closer to the submission deadline: GECCO 2020.
Important Dates
Important Dates
Submission opening: February 27, 2020
Submission deadline: April 03, 2020 (Extended) April 10,2020
Acceptance notification: April 17, 2020
Camera-ready and registration: April 24, 2020
Workshop date: TBC depending on GECCO program schedule (July 8 or 12, 2020)
There will be NO EXTENSIONS to any of the deadlines
Accepted papers
Accepted papers
Incremental Lattice Design of Weight Vector Set. Tomoaki takagi, Heiki Takadama, Hiroyuki Sato
Multi-objective Optimization in the Agile Software Project Scheduling using Decomposition. Saúl Zapotecas-Martínez, Abel Gacía Nájera, and Humberto Cervantes
Organizers
Organizers
Google Sites
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