Age-Layer-Population-Structure with Self-adaptation in Optimization

Kaifeng Yang; Bernhard Werth; Michael Affenzeller

doi:10.1007/978-3-031-83885-9_1

Age-Layer-Population-Structure with Self-adaptation in Optimization

Kaifeng Yang^*, Bernhard Werth, Michael Affenzeller

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

Abstract

Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers
Editors	Alexis Quesada-Arencibia, Michael Affenzeller, Roberto Moreno-Díaz
Publisher	Springer
Pages	3-11
Number of pages	9
ISBN (Print)	9783031838873
DOIs	https://doi.org/10.1007/978-3-031-83885-9_1
Publication status	Published - 2025
Event	19th International Conference on Computer Aided Systems Theory, EUROCAST 2024 - Las Palmas de Canaria, Spain Duration: 25 Feb 2024 → 1 Mar 2024

Publication series

Name	Lecture Notes in Computer Science
Volume	15174 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computer Aided Systems Theory, EUROCAST 2024
Country/Territory	Spain
City	Las Palmas de Canaria
Period	25.02.2024 → 01.03.2024

Keywords

ALPS
Evolutionary Algorithms
Self-adaptation

Access to Document

10.1007/978-3-031-83885-9_1

Cite this

Yang, K., Werth, B., & Affenzeller, M. (2025). Age-Layer-Population-Structure with Self-adaptation in Optimization. In A. Quesada-Arencibia, M. Affenzeller, & R. Moreno-Díaz (Eds.), Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers (pp. 3-11). (Lecture Notes in Computer Science; Vol. 15174 LNCS). Springer. https://doi.org/10.1007/978-3-031-83885-9_1

Yang, Kaifeng ; Werth, Bernhard ; Affenzeller, Michael. / Age-Layer-Population-Structure with Self-adaptation in Optimization. Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. editor / Alexis Quesada-Arencibia ; Michael Affenzeller ; Roberto Moreno-Díaz. Springer, 2025. pp. 3-11 (Lecture Notes in Computer Science).

@inproceedings{7b49925552414f54b9d2745fd2deb401,

title = "Age-Layer-Population-Structure with Self-adaptation in Optimization",

abstract = "Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer{\textquoteright}s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems.",

keywords = "ALPS, Evolutionary Algorithms, Self-adaptation",

author = "Kaifeng Yang and Bernhard Werth and Michael Affenzeller",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 19th International Conference on Computer Aided Systems Theory, EUROCAST 2024 ; Conference date: 25-02-2024 Through 01-03-2024",

year = "2025",

doi = "10.1007/978-3-031-83885-9_1",

language = "English",

isbn = "9783031838873",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "3--11",

editor = "Alexis Quesada-Arencibia and Michael Affenzeller and Roberto Moreno-D{\'i}az",

booktitle = "Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers",

address = "Germany",

}

Yang, K, Werth, B & Affenzeller, M 2025, Age-Layer-Population-Structure with Self-adaptation in Optimization. in A Quesada-Arencibia, M Affenzeller & R Moreno-Díaz (eds), Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. Lecture Notes in Computer Science, vol. 15174 LNCS, Springer, pp. 3-11, 19th International Conference on Computer Aided Systems Theory, EUROCAST 2024, Las Palmas de Canaria, Spain, 25.02.2024. https://doi.org/10.1007/978-3-031-83885-9_1

Age-Layer-Population-Structure with Self-adaptation in Optimization. / Yang, Kaifeng; Werth, Bernhard ; Affenzeller, Michael.
Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. ed. / Alexis Quesada-Arencibia; Michael Affenzeller; Roberto Moreno-Díaz. Springer, 2025. p. 3-11 (Lecture Notes in Computer Science; Vol. 15174 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Age-Layer-Population-Structure with Self-adaptation in Optimization

AU - Yang, Kaifeng

AU - Werth, Bernhard

AU - Affenzeller, Michael

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems.

AB - Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems.

KW - ALPS

KW - Evolutionary Algorithms

KW - Self-adaptation

UR - http://www.scopus.com/inward/record.url?scp=105004407837&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-83885-9_1

DO - 10.1007/978-3-031-83885-9_1

M3 - Conference contribution

AN - SCOPUS:105004407837

SN - 9783031838873

T3 - Lecture Notes in Computer Science

SP - 3

EP - 11

BT - Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers

A2 - Quesada-Arencibia, Alexis

A2 - Affenzeller, Michael

A2 - Moreno-Díaz, Roberto

PB - Springer

T2 - 19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

Y2 - 25 February 2024 through 1 March 2024

ER -

Yang K, Werth B , Affenzeller M. Age-Layer-Population-Structure with Self-adaptation in Optimization. In Quesada-Arencibia A, Affenzeller M, Moreno-Díaz R, editors, Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. Springer. 2025. p. 3-11. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-83885-9_1