A black-box discrete optimization benchmarking (BB-DOB) pipeline survey
Aleš Zamuda, Miguel Nicolau, Christine Zarges · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
This paper provides a taxonomical identification survey of classes in discrete optimization challenges that can be found in the literature including a proposed pipeline for benchmarking, inspired by previous computational optimization competitions. Thereby, a Black-Box Discrete Optimization Benchmarking (BB-DOB) perspective is presented for the [email protected] Workshop. It is motivated why certain classes together with their properties (like deception and separability or toy problem label) should be included in the perspective. Moreover, guidelines on how to select significant instances within these classes, the design of experiments setup, performance measures, and presentation methods and formats are discussed.