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A black-box discrete optimization benchmarking (BB-DOB) pipeline survey
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
doi:10.1145/3205651.3208307
dblp:conf/gecco/ZamudaNZ18
fatcat:4olviuc3svflhjg5k2rswzwoqm