ENUM - Evolutionary Numerical Optimization
Description
ENUM (Evolutionary NUMerical Optimization) focuses on optimization algorithms operating in continuous search spaces, particularly those that do not rely on derivative information. The track includes (but is not limited to) stochastic and randomized approaches such as Differential Evolution (DE), Evolution Strategies (ES, including CMA-ES), Estimation-of-Distribution Algorithms (EDAs), and Particle Swarm Optimization (PSO). The track is concerned with a wide range of optimization settings, including unconstrained, constrained, and noisy problems, as well as mixed-domain scenarios involving discrete and continuous variables or uncertainty. Contributions of interest include all aspects of evolutionary numerical optimization, including algorithmic design, theoretical foundations, and benchmarking methodologies.
Scope
The ENUM track invites submissions that present original work regarding theoretical analysis, algorithmic design, and experimental validation of algorithms for optimization in continuous domains, including work on large-scale and budgeted optimization, handling of constraints, multi-modality, noise, surrogate modelling techniques, uncertain and/or changing environments, and mixed-integer problems. Work that advances experimental methodology and benchmarking or problem and search space analysis is also encouraged.
The ENUM track may be appropriate for papers concerned with a particular real-world optimization problem with continuous search space, provided that the paper's main focus is on methodological aspects, or if one or more "real-world-like" problems are used as a testbed for comparing of several relevant methods. If the primary contribution is the solution of the problem, then it should be submitted to the Real-World Applications (RWA) track.
Papers dealing with theoretical analyses of evolutionary algorithms in continuous search spaces may be submitted primarily to the ENUM track with the theory track as a secondary track, or the other way around.
Track Chairs
Anne Auger
Inria, France | webpage
Anne Auger is a Director of Research at Inria and a Professor at École Polytechnique (Institut Polytechnique de Paris). Her research focuses on numerical optimization and black-box optimization, with a particular emphasis on evolution strategies and theory-driven algorithm design. She has contributed extensively to the foundations and benchmarking of evolutionary algorithms. She served as General Chair of GECCO 2019 and has long been an active member of the ACM SIGEVO community, where she serves on the SIGEVO Executive and served the Business Committees.Dirk Arnold
Dalhousie University
Dirk Arnold is a Professor in the Faculty of Computer Science at Dalhousie University. His research interests include evolutionary computation and numerical optimization. He is an Associate Editor of Evolutionary Computation, an Area Editor of the ACM Transactions on Evolutionary Learning and Optimization, and he was General Chair of GECCO 2014.