A black-box discrete optimization benchmarking (BB-DOB) pipeline survey

Aleš Zamuda, Miguel Nicolau, Christine Zarges
2018 Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18  
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 BB-DOB@GECCO Workshop. It is motivated why certain classes together with their properties (like deception and separability or toy problem label) should
more » ... e 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.
doi:10.1145/3205651.3208307 dblp:conf/gecco/ZamudaNZ18 fatcat:4olviuc3svflhjg5k2rswzwoqm