Increasing genetic programming robustness using simulated dunning-kruger effect

Thomas D. Griffiths; Anikó Ekárt

doi:10.1145/3319619.3321885

Increasing genetic programming robustness using simulated dunning-kruger effect

Thomas D. Griffiths, Anikó Ekárt

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Robustness is a key characteristic of genetic programming candidate solutions, providing the ability to maintain a satisfactory level of performance under dynamic and uncertain environments. In this paper we perform experiments on Tartarus problem instances[2] exploring the hypothesis that the introduction of a fitness distribution bias, inspired by the Dunning-Kruger effect [5], will lead to an increase in the diversity and robustness of candidate solutions.

Original language	English
Title of host publication	GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
Publisher	ACM
Pages	340-341
Number of pages	2
ISBN (Electronic)	9781450367486
ISBN (Print)	978-1-4503-6748-6
DOIs	https://doi.org/10.1145/3319619.3321885
Publication status	Published - 13 Jul 2019
Event	the Genetic and Evolutionary Computation Conference Companion - Prague, Czech Republic Duration: 13 Jul 2019 → 17 Jul 2019

Conference

Conference	the Genetic and Evolutionary Computation Conference Companion
Period	13/07/19 → 17/07/19

Bibliographical note

Keywords

Diversity
Dunning-Kruger
Robustness
Tartarus Problem

Access to Document

10.1145/3319619.3321885

Cite this

@inproceedings{f7dd0a04b940425badb861fd7cb22d15,

title = "Increasing genetic programming robustness using simulated dunning-kruger effect",

abstract = "Robustness is a key characteristic of genetic programming candidate solutions, providing the ability to maintain a satisfactory level of performance under dynamic and uncertain environments. In this paper we perform experiments on Tartarus problem instances[2] exploring the hypothesis that the introduction of a fitness distribution bias, inspired by the Dunning-Kruger effect [5], will lead to an increase in the diversity and robustness of candidate solutions.",

keywords = "Diversity, Dunning-Kruger, Robustness, Tartarus Problem",

author = "Griffiths, {Thomas D.} and Anik{\'o} Ek{\'a}rt",

note = "{\textcopyright} 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.; the Genetic and Evolutionary Computation Conference Companion ; Conference date: 13-07-2019 Through 17-07-2019",

year = "2019",

month = jul,

day = "13",

doi = "10.1145/3319619.3321885",

language = "English",

isbn = "978-1-4503-6748-6",

pages = "340--341",

booktitle = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",

publisher = "ACM",

address = "United States",

}

TY - GEN

T1 - Increasing genetic programming robustness using simulated dunning-kruger effect

AU - Griffiths, Thomas D.

AU - Ekárt, Anikó

PY - 2019/7/13

Y1 - 2019/7/13

N2 - Robustness is a key characteristic of genetic programming candidate solutions, providing the ability to maintain a satisfactory level of performance under dynamic and uncertain environments. In this paper we perform experiments on Tartarus problem instances[2] exploring the hypothesis that the introduction of a fitness distribution bias, inspired by the Dunning-Kruger effect [5], will lead to an increase in the diversity and robustness of candidate solutions.

AB - Robustness is a key characteristic of genetic programming candidate solutions, providing the ability to maintain a satisfactory level of performance under dynamic and uncertain environments. In this paper we perform experiments on Tartarus problem instances[2] exploring the hypothesis that the introduction of a fitness distribution bias, inspired by the Dunning-Kruger effect [5], will lead to an increase in the diversity and robustness of candidate solutions.

KW - Diversity

KW - Dunning-Kruger

KW - Robustness

KW - Tartarus Problem

UR - http://dl.acm.org/citation.cfm?doid=3319619.3321885

UR - http://www.scopus.com/inward/record.url?scp=85070620106&partnerID=8YFLogxK

U2 - 10.1145/3319619.3321885

DO - 10.1145/3319619.3321885

M3 - Conference publication

SN - 978-1-4503-6748-6

SP - 340

EP - 341

BT - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

PB - ACM

T2 - the Genetic and Evolutionary Computation Conference Companion

Y2 - 13 July 2019 through 17 July 2019

ER -

Increasing genetic programming robustness using simulated dunning-kruger effect

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this