Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results

Zimin Liang; Miqing Li; Per Kristian Lehre

doi:10.1145/3583133.3590646

Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results

Zimin Liang, Miqing Li^*, Per Kristian Lehre

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective evolutionary algorithms (MOEAs) in the late 1990s. In this paper, we take an opposite perspective to conduct the population update in MOEAs by simply discarding elitism. That is, we treat the newly-generated solutions as the new population directly (so that all selection pressure comes from mating selection). We propose a simple non-elitist MOEA (called NE-MOEA) that only uses Pareto dominance sorting to compare solutions, without involving any diversity-related selection criterion. Preliminary experimental results show that NE-MOEA can compete with well-known elitist MOEAs (NSGA-II, SMS-EMOA and NSGA-III) on several combinatorial problems. Lastly, we discuss limitations of the proposed non-elitist algorithm and suggest possible future research directions.

Original language	English
Title of host publication	GECCO '23 Companion
Subtitle of host publication	Proceedings of the Companion Conference on Genetic and Evolutionary Computation
Publisher	Association for Computing Machinery (ACM)
Pages	383-386
Number of pages	4
ISBN (Electronic)	9798400701207
DOIs	https://doi.org/10.1145/3583133.3590646
Publication status	Published - 24 Jul 2023
Event	GECCO '23: Genetic and Evolutionary Computation Conference - Lisbon, Portugal Duration: 15 Jul 2023 → 19 Jul 2023 https://dl.acm.org/conference/gecco

Publication series

Name	GECCO: Genetic and Evolutionary Computation Conference

Conference

Conference	GECCO '23: Genetic and Evolutionary Computation Conference
Abbreviated title	GECCO '23
Country/Territory	Portugal
City	Lisbon
Period	15/07/23 → 19/07/23
Internet address	https://dl.acm.org/conference/gecco

Bibliographical note

Publisher Copyright:
© 2023 Copyright held by the owner/author(s).

Keywords

elitism
Evolutionary algorithms
multi-objective optimisation
population update

ASJC Scopus subject areas

Software
Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1145/3583133.3590646

Cite this

Liang, Z., Li, M., & Lehre, P. K. (2023). Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 383-386). (GECCO: Genetic and Evolutionary Computation Conference). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583133.3590646

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title = "Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results",

abstract = "Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective evolutionary algorithms (MOEAs) in the late 1990s. In this paper, we take an opposite perspective to conduct the population update in MOEAs by simply discarding elitism. That is, we treat the newly-generated solutions as the new population directly (so that all selection pressure comes from mating selection). We propose a simple non-elitist MOEA (called NE-MOEA) that only uses Pareto dominance sorting to compare solutions, without involving any diversity-related selection criterion. Preliminary experimental results show that NE-MOEA can compete with well-known elitist MOEAs (NSGA-II, SMS-EMOA and NSGA-III) on several combinatorial problems. Lastly, we discuss limitations of the proposed non-elitist algorithm and suggest possible future research directions.",

keywords = "elitism, Evolutionary algorithms, multi-objective optimisation, population update",

author = "Zimin Liang and Miqing Li and Lehre, {Per Kristian}",

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Liang, Z, Li, M & Lehre, PK 2023, Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 383-386, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590646

Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results. / Liang, Zimin; Li, Miqing ; Lehre, Per Kristian.
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. Association for Computing Machinery (ACM), 2023. p. 383-386 (GECCO: Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective evolutionary algorithms (MOEAs) in the late 1990s. In this paper, we take an opposite perspective to conduct the population update in MOEAs by simply discarding elitism. That is, we treat the newly-generated solutions as the new population directly (so that all selection pressure comes from mating selection). We propose a simple non-elitist MOEA (called NE-MOEA) that only uses Pareto dominance sorting to compare solutions, without involving any diversity-related selection criterion. Preliminary experimental results show that NE-MOEA can compete with well-known elitist MOEAs (NSGA-II, SMS-EMOA and NSGA-III) on several combinatorial problems. Lastly, we discuss limitations of the proposed non-elitist algorithm and suggest possible future research directions.

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Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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