Decomposition Techniques in Evolutionary Optimization
Webpage: https://sites.google.com/view/dteo/
Description
Decomposition-based optimization involves transforming a complex problem into multiple smaller, more manageable sub-problems that can be solved cooperatively. The evolutionary computing community actively develops methods to explicitly or implicitly design decomposition across four key facets: (i) environmental parameters, (ii) decision variables, (iii) objective functions, and (iv) available computing resources.
This workshop aims to bring together recent advances in the design, analysis, and understanding of evolutionary decomposition techniques. It also provides a platform to discuss challenges in applying decomposition to increasingly large and complex optimization tasks—such as problems with many variables or objectives, multi-modal problems, simulation-based optimization, and uncertain scenarios—while considering modern large-scale computing environments, including massively parallel and decentralized systems.
The workshop focuses on, but is not limited to, the following topics:
- Large-scale evolutionary decomposition: Decomposition in decision space, gray-box methods, co-evolutionary algorithms, grouping techniques, and cooperative methods for constraint handling.
- Many- and multi-objective decomposition: Aggregation and scalarization methods, hybrid island-based approaches, and (sub-)population decomposition and mapping.
- Parallel and distributed evolutionary decomposition: Scalability across decision and objective spaces, decentralized divide-and-conquer strategies, distributed computing efforts, and deployment on heterogeneous, large-scale parallel platforms.
- New general-purpose decomposition techniques: Machine-learning-assisted decomposition, online and offline configuration, search region decomposition, use of multiple surrogates, and parallel approaches for expensive optimization.
- Emerging applications of evolutionary techniques based on decomposition.
- Understanding and benchmarking decomposition techniques.
- Software tools and libraries for evolutionary decomposition.
In general, this workshop encourages both theoretical and practical contributions, focusing on developmental, implementation, and applied aspects of decomposition techniques in evolutionary optimization.
Organizers
Bilel Derbel is a Professor, at the Department of Computer Science at the University of Lille, France. He is the team leader of BONUS (Big Optimization aNd Ultra-Scale Computing), a joint research group the Inria research center of the University of Lille, and the CRIStAL CNRS Laboratory, France. He is a Collaborative Professor at Shinshu University, Nagano, Japan. His current research topics are on the design and analysis of algorithms for solving single- and multi- objective optimization problems using high-level optimization techniques, such as, stochastic search heuristics, ML-inspired search techniques, fitness landscape, parallel and distributed computing.
Ke Li is a UKRI Future Leaders Fellow, a Turing Fellow, and a Senior Lecturer of Computer Science at the University of Exeter (UoE). He was the founding chair of IEEE Computational Intelligence Society (CIS) Task Force 12 from 2017 to 2021. This is an international consortium that brings together global researchers to promote an active state of themed areas of the decomposition-based techniques in CI. Related activities include workshops and special sessions associated with major conferences in EC (GECCO, CEC and PPSN) and webinars since 2018. He has been a Publication Chair of EMO 2021, a dedicated conference on evolutionary multi-objective optimization and multi-criterion decision-making. Moreover, he has been an academic mentor of early career researchers (ECRs) in IEEE CIS since 2020. He has co-chaired a summer school in 2021 themed on "Data-Driven AI/CI: Theory and Applications" sponsored by IEEE Computational Intelligence Society. This summer school aims to i) provide a unique opportunity to ECRs from around the world to learn about state-of-the-art AI/CI techniques and applications; ii) interact with world-renowned high-profile experts; and iii) inspire new ideas and collaborations. Dr Li has been serving as an Associate Editor of four academic journals including IEEE Trans. Evol. Comput. Moreover, he was a guest editor of a special issues on the topic of semantic computing and personalization in Neurocomputing journal and a special issue on the topic of advances of AI in visual systems in Multimedia Tools and Applications journal.
Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in Artificial Intelligence from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Computing Technologies, RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, deep learning, data analytics, multiobjective optimization, operational research, and swarm intelligence. He served as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Fellow.
Saúl Zapotecas-Martínez is an Associate Professor in the Computer Science Department at the National Institute of Astrophysics, Optics, and Electronics (INAOE) in Mexico. He is also a Collaborative Professor at Shinshu University in Nagano, Japan, working with MODO: International Associated Laboratory (Massive Optimization and Computational Intelligence). Dr. Zapotecas serves as an Associate Editor for four academic journals, including IEEE Transactions on Evolutionary Computation and Swarm and Evolutionary Computation. He actively participates in the program committees of numerous international conferences and reviews for several leading journals in evolutionary computation. His research focuses on evolutionary computing, machine learning, neuroevolution, and their applications to complex optimization problems.
Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research (MOP) Group in City University of Hong Kong. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 and 2017 highly cited researchers in computer science. He is an IEEE fellow.