@inproceedings{f4552e8eab8746c290dc1199089f2f3d,
title = "The impact of search volume on the performance of RANDOMSEARCH on the bi-objective BBOB-2016 test suite",
abstract = "Pure random search is undeniably the simplest stochastic search algorithm for numerical optimization. Essentially the only thing to be determined to implement the algo- rithm is its sampling space, the inuence of which on the performance on the bi-objective bbob-biobj test suite of the COCO platform is investigated here. It turns out that the suggested region of interest of [-100; 100]n (with n being the problem dimension) has a too vast volume for the algorithm to approximate the Pareto set effectively. Better performance can be achieved if solutions are sampled uniformly within [-5; 5]n or [-4; 4]n. The latter sampling box corresponds to the smallest hypercube encapsulating all single-objective optima of the 55 constructed bi-objective problems of the bbob-biobj test suite. However, not all best known Pareto set approximations are entirely contained within [-5; 5]n.",
keywords = "Benchmarking, Bi-objective optimization, 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 = "10.1145/2908961.2931709",
language = "English",
series = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "1257--1264",
editor = "Tobias Friedrich",
booktitle = "GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
}