The submission deadline has been extended to August 31, 2021.
Benchmarking plays an important role in the study of evolutionary computation methods and other optimization algorithms. Among other benefits, benchmarking helps us analyze strengths and weaknesses of different techniques — knowledge that can be used to design more efficient optimization approaches. Core to benchmarking is a well-designed experimental setup, which ranges from the selection of algorithms, problem instances, and performance metrics over efficient experimentation to a sound evaluation of the benchmark data. To assist researchers and users of evolutionary computation methods, a number of different tools addressing the various different aspects of benchmarking are available. However, most of them are developed in isolation, without a possible integration to already existing software in mind. This hinders knowledge transfer between the different research groups and between academic and industrial practitioners of evolutionary computation methods.
The goal of this special issue is to provide an overview of state-of-the-art software packages, methods, and data sets that facilitate sound benchmarking of evolutionary algorithms and other optimization techniques. By providing an overview of today’s benchmarking landscape, new synergies will be laid open, helping the community to converge towards a higher compatibility between tools, towards better reproducibility and replicability of our research, a better use of resources, and, ultimately, towards higher standards in our benchmarking practices.