Newfoundland
Traditionally, a genetic algorithm is used to analyze networks by maximizing the modularity (Q) measure to create a favorable community. A coevolutionary algorithm is used here to not only find the appropriate community division for a network, but to find interesting networks containing substantial...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.221.2535 2023-05-15T17:22:22+02:00 Newfoundland Garnett Wilson Rodolphe Devillers Simon Harding Wolfgang Banzhaf Orland Hoeber The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2535 http://www.cs.mun.ca/%7Ebanzhaf/papers/Fisheries-GECCO2011.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2535 http://www.cs.mun.ca/%7Ebanzhaf/papers/Fisheries-GECCO2011.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.cs.mun.ca/%7Ebanzhaf/papers/Fisheries-GECCO2011.pdf I.2.8 [Artificial Intelligence Problem Solving Control text ftciteseerx 2016-01-07T18:19:26Z Traditionally, a genetic algorithm is used to analyze networks by maximizing the modularity (Q) measure to create a favorable community. A coevolutionary algorithm is used here to not only find the appropriate community division for a network, but to find interesting networks containing substantial changes in data within a very large network space. The network is one of the largest, if not the largest, analyzed by evolutionary computation techniques to date and is created using a real world data set consisting of fisheries catch data in the north Atlantic Ocean off the coast of Canada. This work examines the quantitative performance of two types of coevolutionary algorithms against both a standard GA that uses a natural (but not necessarily optimal) division of the data set into communities, and simulated annealing. The goal for all search algorithms was to automatically find anomalies (differences in catch) within the data. To measure practical usefulness of the system, a fisheries expert analyzed the best networks located by the search algorithms using an existing visualization software prototype. The expert indicated that a refined version of coevolutionary GA known as PAMDGA was found to most reliably locate subnetworks containing catch differences of biological relevance. Text Newfoundland North Atlantic Unknown Canada |
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I.2.8 [Artificial Intelligence Problem Solving Control |
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I.2.8 [Artificial Intelligence Problem Solving Control Garnett Wilson Rodolphe Devillers Simon Harding Wolfgang Banzhaf Orland Hoeber Newfoundland |
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I.2.8 [Artificial Intelligence Problem Solving Control |
description |
Traditionally, a genetic algorithm is used to analyze networks by maximizing the modularity (Q) measure to create a favorable community. A coevolutionary algorithm is used here to not only find the appropriate community division for a network, but to find interesting networks containing substantial changes in data within a very large network space. The network is one of the largest, if not the largest, analyzed by evolutionary computation techniques to date and is created using a real world data set consisting of fisheries catch data in the north Atlantic Ocean off the coast of Canada. This work examines the quantitative performance of two types of coevolutionary algorithms against both a standard GA that uses a natural (but not necessarily optimal) division of the data set into communities, and simulated annealing. The goal for all search algorithms was to automatically find anomalies (differences in catch) within the data. To measure practical usefulness of the system, a fisheries expert analyzed the best networks located by the search algorithms using an existing visualization software prototype. The expert indicated that a refined version of coevolutionary GA known as PAMDGA was found to most reliably locate subnetworks containing catch differences of biological relevance. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Garnett Wilson Rodolphe Devillers Simon Harding Wolfgang Banzhaf Orland Hoeber |
author_facet |
Garnett Wilson Rodolphe Devillers Simon Harding Wolfgang Banzhaf Orland Hoeber |
author_sort |
Garnett Wilson |
title |
Newfoundland |
title_short |
Newfoundland |
title_full |
Newfoundland |
title_fullStr |
Newfoundland |
title_full_unstemmed |
Newfoundland |
title_sort |
newfoundland |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2535 http://www.cs.mun.ca/%7Ebanzhaf/papers/Fisheries-GECCO2011.pdf |
geographic |
Canada |
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Canada |
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Newfoundland North Atlantic |
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Newfoundland North Atlantic |
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http://www.cs.mun.ca/%7Ebanzhaf/papers/Fisheries-GECCO2011.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2535 http://www.cs.mun.ca/%7Ebanzhaf/papers/Fisheries-GECCO2011.pdf |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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