@inproceedings{e01a774b302b4a1689266a2d9f61afd5,
title = "Limited memory, limited arity unbiased black-box complexity: First insights",
abstract = "Limited arity unbiased black-box complexity was proven to be a successful tool for understanding the working principles of randomized search heuristics and delivered insights to develop new efficient algorithms. While good upper bounds for simple problems were found long time ago, there are still no matching lower bounds. On a road towards closing this gap, we introduce the notion of limited-memory, limited arity unbiased black-box complexity. We show that some efficient binary unbiased algorithms (almost) satisfy the memory-2 requirement, and present an algorithm to compute, for a given problem size, the exact lower bound on the runtime of any memory-m k-ary algorithm on any unimodal function.",
keywords = "Memory-resticted, Unbiased complexity, Unimodal functions",
author = "Nina Bulanova and Maxim Buzdalov",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 ; Conference date: 13-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
day = "13",
doi = "10.1145/3319619.3326903",
language = "English",
series = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery",
pages = "2020--2023",
booktitle = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
address = "United States of America",
}