ENUM - Evolutionary Numerical Optimization

Description

The ENUM track (Evolutionary NUMerical optimization) is concerned with randomized search algorithms and continuous search spaces. The scope of the ENUM track includes, but is not limited to, stochastic methods such as differential evolution (DE), evolution strategies (ES), estimation-of-distribution algorithms (EDAs) and particle swarm optimization (PSO). The track is also concerned with the analyses of continuous search spaces to better understand the complexity of optimization problems and benchmarking of continuous optimization.

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, uncertain and/or changing environments, and mixed-integer problems. Work that advances experimental methodology and benchmarking, problem and search space analysis is also encouraged.

Application papers reporting on solving a particular real-world optimization problem with continuous search space, with a relevant methodology, should be sent primarily to the Real-World Applications (RWA) track, with ENUM being a possible secondary track. On the other hand, if one or more "real-world-like" problems are used as a testbed for a comparison of several relevant methods, ENUM is the right primary track.

Papers dealing with theoretical analyses of evolutionary algorithms in continuous search spaces can be submitted primarily to the ENUM track with the theory track as a secondary track, or the other way round.