Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization
Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for loca...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ishibuchi, Hisao [verfasserIn] Sakane, Yuji [verfasserIn] Tsukamoto, Noritaka [verfasserIn] Nojima, Yusuke [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 15(2010), 9 vom: 12. Juni, Seite 1749-1767 |
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| Übergeordnetes Werk: |
volume:15 ; year:2010 ; number:9 ; day:12 ; month:06 ; pages:1749-1767 |
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10.1007/s00500-010-0617-8 |
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| 520 | |a Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. | ||
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| 650 | 4 | |a Neighborhood structures |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Multi-objective optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sakane, Yuji |e verfasserin |4 aut | |
| 700 | 1 | |a Tsukamoto, Noritaka |e verfasserin |4 aut | |
| 700 | 1 | |a Nojima, Yusuke |e verfasserin |4 aut | |
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10.1007/s00500-010-0617-8 doi (DE-627)SPR006479537 (SPR)s00500-010-0617-8-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. Cellular genetic algorithms (dpeaa)DE-He213 Neighborhood structures (dpeaa)DE-He213 Structured demes (dpeaa)DE-He213 Single-objective optimization (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Sakane, Yuji verfasserin aut Tsukamoto, Noritaka verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 9 vom: 12. Juni, Seite 1749-1767 (DE-627)SPR006469531 nnns volume:15 year:2010 number:9 day:12 month:06 pages:1749-1767 https://dx.doi.org/10.1007/s00500-010-0617-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 9 12 06 1749-1767 |
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10.1007/s00500-010-0617-8 doi (DE-627)SPR006479537 (SPR)s00500-010-0617-8-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. Cellular genetic algorithms (dpeaa)DE-He213 Neighborhood structures (dpeaa)DE-He213 Structured demes (dpeaa)DE-He213 Single-objective optimization (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Sakane, Yuji verfasserin aut Tsukamoto, Noritaka verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 9 vom: 12. Juni, Seite 1749-1767 (DE-627)SPR006469531 nnns volume:15 year:2010 number:9 day:12 month:06 pages:1749-1767 https://dx.doi.org/10.1007/s00500-010-0617-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 9 12 06 1749-1767 |
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10.1007/s00500-010-0617-8 doi (DE-627)SPR006479537 (SPR)s00500-010-0617-8-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. Cellular genetic algorithms (dpeaa)DE-He213 Neighborhood structures (dpeaa)DE-He213 Structured demes (dpeaa)DE-He213 Single-objective optimization (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Sakane, Yuji verfasserin aut Tsukamoto, Noritaka verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 9 vom: 12. Juni, Seite 1749-1767 (DE-627)SPR006469531 nnns volume:15 year:2010 number:9 day:12 month:06 pages:1749-1767 https://dx.doi.org/10.1007/s00500-010-0617-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 9 12 06 1749-1767 |
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10.1007/s00500-010-0617-8 doi (DE-627)SPR006479537 (SPR)s00500-010-0617-8-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. Cellular genetic algorithms (dpeaa)DE-He213 Neighborhood structures (dpeaa)DE-He213 Structured demes (dpeaa)DE-He213 Single-objective optimization (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Sakane, Yuji verfasserin aut Tsukamoto, Noritaka verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 9 vom: 12. Juni, Seite 1749-1767 (DE-627)SPR006469531 nnns volume:15 year:2010 number:9 day:12 month:06 pages:1749-1767 https://dx.doi.org/10.1007/s00500-010-0617-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 9 12 06 1749-1767 |
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10.1007/s00500-010-0617-8 doi (DE-627)SPR006479537 (SPR)s00500-010-0617-8-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. Cellular genetic algorithms (dpeaa)DE-He213 Neighborhood structures (dpeaa)DE-He213 Structured demes (dpeaa)DE-He213 Single-objective optimization (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Sakane, Yuji verfasserin aut Tsukamoto, Noritaka verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 9 vom: 12. Juni, Seite 1749-1767 (DE-627)SPR006469531 nnns volume:15 year:2010 number:9 day:12 month:06 pages:1749-1767 https://dx.doi.org/10.1007/s00500-010-0617-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 9 12 06 1749-1767 |
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implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization |
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Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization |
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Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. |
| abstractGer |
Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. |
| abstract_unstemmed |
Abstract In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms. |
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Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization |
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https://dx.doi.org/10.1007/s00500-010-0617-8 |
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Sakane, Yuji Tsukamoto, Noritaka Nojima, Yusuke |
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Sakane, Yuji Tsukamoto, Noritaka Nojima, Yusuke |
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10.1007/s00500-010-0617-8 |
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