Evolutionary search for understanding movement dynamics on mixed networks
Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Spears, William M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Geoinformatica - Springer US, 1997, 17(2012), 2 vom: 11. Apr., Seite 353-385 |
|---|---|
| Übergeordnetes Werk: |
volume:17 ; year:2012 ; number:2 ; day:11 ; month:04 ; pages:353-385 |
| Links: |
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| DOI / URN: |
10.1007/s10707-012-0155-x |
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OLC2038963029 |
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| 520 | |a Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. | ||
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10.1007/s10707-012-0155-x doi (DE-627)OLC2038963029 (DE-He213)s10707-012-0155-x-p DE-627 ger DE-627 rakwb eng 550 VZ Spears, William M. verfasserin aut Evolutionary search for understanding movement dynamics on mixed networks 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. Network analysis Evolutionary algorithms Functional paths Multicriteria shortest paths Dynamic routing Prager, Steven D. aut Enthalten in Geoinformatica Springer US, 1997 17(2012), 2 vom: 11. Apr., Seite 353-385 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:17 year:2012 number:2 day:11 month:04 pages:353-385 https://doi.org/10.1007/s10707-012-0155-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_4318 AR 17 2012 2 11 04 353-385 |
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10.1007/s10707-012-0155-x doi (DE-627)OLC2038963029 (DE-He213)s10707-012-0155-x-p DE-627 ger DE-627 rakwb eng 550 VZ Spears, William M. verfasserin aut Evolutionary search for understanding movement dynamics on mixed networks 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. Network analysis Evolutionary algorithms Functional paths Multicriteria shortest paths Dynamic routing Prager, Steven D. aut Enthalten in Geoinformatica Springer US, 1997 17(2012), 2 vom: 11. Apr., Seite 353-385 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:17 year:2012 number:2 day:11 month:04 pages:353-385 https://doi.org/10.1007/s10707-012-0155-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_4318 AR 17 2012 2 11 04 353-385 |
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10.1007/s10707-012-0155-x doi (DE-627)OLC2038963029 (DE-He213)s10707-012-0155-x-p DE-627 ger DE-627 rakwb eng 550 VZ Spears, William M. verfasserin aut Evolutionary search for understanding movement dynamics on mixed networks 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. Network analysis Evolutionary algorithms Functional paths Multicriteria shortest paths Dynamic routing Prager, Steven D. aut Enthalten in Geoinformatica Springer US, 1997 17(2012), 2 vom: 11. Apr., Seite 353-385 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:17 year:2012 number:2 day:11 month:04 pages:353-385 https://doi.org/10.1007/s10707-012-0155-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_4318 AR 17 2012 2 11 04 353-385 |
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10.1007/s10707-012-0155-x doi (DE-627)OLC2038963029 (DE-He213)s10707-012-0155-x-p DE-627 ger DE-627 rakwb eng 550 VZ Spears, William M. verfasserin aut Evolutionary search for understanding movement dynamics on mixed networks 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. Network analysis Evolutionary algorithms Functional paths Multicriteria shortest paths Dynamic routing Prager, Steven D. aut Enthalten in Geoinformatica Springer US, 1997 17(2012), 2 vom: 11. Apr., Seite 353-385 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:17 year:2012 number:2 day:11 month:04 pages:353-385 https://doi.org/10.1007/s10707-012-0155-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_4318 AR 17 2012 2 11 04 353-385 |
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Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. © Springer Science+Business Media, LLC 2012 |
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Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. © Springer Science+Business Media, LLC 2012 |
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Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. © Springer Science+Business Media, LLC 2012 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2038963029</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503062821.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2012 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10707-012-0155-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2038963029</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10707-012-0155-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Spears, William M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evolutionary search for understanding movement dynamics on mixed networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2012</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. 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