Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem

Sebastian Josef Raggl; Andreas Beham; Stefan Wagner; Michael Affenzeller

doi:10.1007/978-3-319-74718-7_42

Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem

Sebastian Josef Raggl, Andreas Beham, Stefan Wagner, Michael Affenzeller

Research Center Hagenberg

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

2 Citations (Scopus)

Abstract

We present a new hybrid model-based algorithm called Memetic Path Relinking (MemPR). MemPR incorporates ideas of memetic, evolutionary, model-based algorithms and path relinking. It uses different operators that compete to fill a small population of high quality solutions. We present a new hard grouping problem derived from a real world transport lot building problem. In order to better understand the algorithm as well as the problem we analyse the impact of the different operators on solution quality and which operators perform best at which stage of optimisation. Finally we compare MemPR to other state-of-the-art algorithms and find that MemPR outperforms them on real-world problem instances.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	347-354
Number of pages	8
ISBN (Print)	9783319747170
DOIs	https://doi.org/10.1007/978-3-319-74718-7_42
Publication status	Published - 2018
Event	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spain Duration: 19 Feb 2017 → 24 Feb 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10671 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	19.02.2017 → 24.02.2017

Keywords

Estimation of distribution algorithm
Grouping problem
Hybrid algorithm
Memetic algorithm

Access to Document

10.1007/978-3-319-74718-7_42

Cite this

Raggl, S. J., Beham, A., Wagner, S., & Affenzeller, M. (2018). Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem. In R. Moreno-Diaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers (pp. 347-354). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671 LNCS). Springer. https://doi.org/10.1007/978-3-319-74718-7_42

Raggl, Sebastian Josef ; Beham, Andreas ; Wagner, Stefan et al. / Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem. Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. editor / Roberto Moreno-Diaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2018. pp. 347-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem",

abstract = "We present a new hybrid model-based algorithm called Memetic Path Relinking (MemPR). MemPR incorporates ideas of memetic, evolutionary, model-based algorithms and path relinking. It uses different operators that compete to fill a small population of high quality solutions. We present a new hard grouping problem derived from a real world transport lot building problem. In order to better understand the algorithm as well as the problem we analyse the impact of the different operators on solution quality and which operators perform best at which stage of optimisation. Finally we compare MemPR to other state-of-the-art algorithms and find that MemPR outperforms them on real-world problem instances.",

keywords = "Estimation of distribution algorithm, Grouping problem, Hybrid algorithm, Memetic algorithm",

author = "Raggl, {Sebastian Josef} and Andreas Beham and Stefan Wagner and Michael Affenzeller",

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doi = "10.1007/978-3-319-74718-7_42",

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Raggl, SJ, Beham, A, Wagner, S & Affenzeller, M 2018, Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem. in R Moreno-Diaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10671 LNCS, Springer, pp. 347-354, 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Las Palmas de Gran Canaria, Spain, 19.02.2017. https://doi.org/10.1007/978-3-319-74718-7_42

Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem. / Raggl, Sebastian Josef; Beham, Andreas; Wagner, Stefan et al.
Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Diaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2018. p. 347-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671 LNCS).

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

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T1 - Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem

AU - Raggl, Sebastian Josef

AU - Beham, Andreas

AU - Wagner, Stefan

AU - Affenzeller, Michael

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N2 - We present a new hybrid model-based algorithm called Memetic Path Relinking (MemPR). MemPR incorporates ideas of memetic, evolutionary, model-based algorithms and path relinking. It uses different operators that compete to fill a small population of high quality solutions. We present a new hard grouping problem derived from a real world transport lot building problem. In order to better understand the algorithm as well as the problem we analyse the impact of the different operators on solution quality and which operators perform best at which stage of optimisation. Finally we compare MemPR to other state-of-the-art algorithms and find that MemPR outperforms them on real-world problem instances.

AB - We present a new hybrid model-based algorithm called Memetic Path Relinking (MemPR). MemPR incorporates ideas of memetic, evolutionary, model-based algorithms and path relinking. It uses different operators that compete to fill a small population of high quality solutions. We present a new hard grouping problem derived from a real world transport lot building problem. In order to better understand the algorithm as well as the problem we analyse the impact of the different operators on solution quality and which operators perform best at which stage of optimisation. Finally we compare MemPR to other state-of-the-art algorithms and find that MemPR outperforms them on real-world problem instances.

KW - Estimation of distribution algorithm

KW - Grouping problem

KW - Hybrid algorithm

KW - Memetic algorithm

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SN - 9783319747170

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 347

EP - 354

BT - Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers

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T2 - 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017

Y2 - 19 February 2017 through 24 February 2017

ER -

Raggl SJ, Beham A, Wagner S , Affenzeller M. Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem. In Moreno-Diaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. Springer. 2018. p. 347-354. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-74718-7_42

Analysing a hybrid model-based evolutionary algorithm for a hard grouping problem

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