@inproceedings{d7baf6114fcd4ce2823fcb18500435a1,
title = "The impact of variation operators on the performance of SMS-EMOA on the bi-objective BBOB-2016 test suite",
abstract = "The S-metric-Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) is one of the best-known indicator-based multi-objective optimization algorithms. It employs the S-metric or hypervolume indicator in its (steady-state) selection by deleting in each iteration the solution that has the smallest contribution to the hypervolume indicator. In the SMS-EMOA, the conceptual idea is this hypervolume-based selection. Hence the algorithm can, for example, be combined with several variation operators. Here, we benchmark two versions of SMS-EMOA which employ differential evolution (DE) and simulated binary crossover (SBX) with polynomial mutation (PM) respectively on the newly introduced bi-objective family bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. The results unsurprisingly reveal that the choice of the variation operator is crucial for performance with a clear advantage of the DE variant on almost all functions.",
keywords = "Benchmarking, Bi-objective optimizatio, Black-box optimization",
author = "Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Dejan Tu{\v s}ar and Tea Tuar and Tobias Wagner",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion ; Conference date: 20-07-2016 Through 24-07-2016",
year = "2016",
month = jul,
day = "20",
doi = "10.1145/2908961.2931705",
language = "English",
series = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "1225--1232",
editor = "Tobias Friedrich",
booktitle = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
}