Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution

Denis Antipov; Maxim Buzdalov; Benjamin Doerr

doi:10.1145/3449639.3459377

Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution

Denis Antipov, Maxim Buzdalov, Benjamin Doerr

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

29 Citations (Scopus)

Original language	English
Title of host publication	GECCO '21
Subtitle of host publication	Proceedings of the Genetic and Evolutionary Computation Conference
Editors	Francisco Chicano
Publisher	Association for Computing Machinery
Pages	1115-1123
Number of pages	9
ISBN (Print)	978-1-4503-8350-9
DOIs	https://doi.org/10.1145/3449639.3459377
Publication status	Published - 26 Jun 2021
Externally published	Yes
Event	GECCO 2021 -Genetic and Evolutionary Computation Conference - Lille, France Duration: 10 Jul 2021 → 14 Jul 2021

Conference

Conference	GECCO 2021 -Genetic and Evolutionary Computation Conference
Country/Territory	France
City	Lille
Period	10 Jul 2021 → 14 Jul 2021

Keywords

crossover
dynamic parameter choice
runtime analysis
theory

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10.1145/3449639.3459377

https://arxiv.org/abs/2104.06714

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@inproceedings{47d98be1fe9749bf87d776b7d3f70f69,

title = "Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution",

keywords = "crossover, dynamic parameter choice, runtime analysis, theory",

author = "Denis Antipov and Maxim Buzdalov and Benjamin Doerr",

year = "2021",

month = jun,

day = "26",

doi = "10.1145/3449639.3459377",

language = "English",

isbn = "978-1-4503-8350-9",

pages = "1115--1123",

editor = "Francisco Chicano",

booktitle = "GECCO '21",

publisher = "Association for Computing Machinery",

address = "United States of America",

note = "GECCO 2021 -Genetic and Evolutionary Computation Conference ; Conference date: 10-07-2021 Through 14-07-2021",

}

Antipov, D, Buzdalov, M & Doerr, B 2021, Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. in F Chicano (ed.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery, pp. 1115-1123, GECCO 2021 -Genetic and Evolutionary Computation Conference , Lille, France, 10 Jul 2021. https://doi.org/10.1145/3449639.3459377

Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. / Antipov, Denis; Buzdalov, Maxim; Doerr, Benjamin.
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. ed. / Francisco Chicano. Association for Computing Machinery, 2021. p. 1115-1123.

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution

AU - Antipov, Denis

AU - Buzdalov, Maxim

AU - Doerr, Benjamin

PY - 2021/6/26

Y1 - 2021/6/26

KW - crossover

KW - dynamic parameter choice

KW - runtime analysis

KW - theory

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85108133321&origin=resultslist

U2 - 10.1145/3449639.3459377

DO - 10.1145/3449639.3459377

M3 - Conference Proceeding (Non-Journal item)

SN - 978-1-4503-8350-9

SP - 1115

EP - 1123

BT - GECCO '21

A2 - Chicano, Francisco

PB - Association for Computing Machinery

T2 - GECCO 2021 -Genetic and Evolutionary Computation Conference

Y2 - 10 July 2021 through 14 July 2021

ER -

Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution

Conference

Keywords

Access to Document

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Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution

Cite this