Exploitation of Linkage Learning in Evolutionary Algorithms:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2010
|
| Schriftenreihe: | Evolutionary Learning and Optimization
3 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHI01 FHN01 FHR01 Volltext |
| Beschreibung: | One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues |
| Beschreibung: | 1 Online-Ressource (265p. 30 illus. in color) |
| ISBN: | 9783642128349 |
| DOI: | 10.1007/978-3-642-12834-9 |
Internformat
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| 505 | 0 | |a Linkage and Problem Structures -- Linkage Structure and Genetic Evolutionary Algorithms -- Fragment as a Small Evidence of the Building Blocks Existence -- Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm -- DEUM – A Fully Multivariate EDA Based on Markov Networks -- Model Building and Exploiting -- Pairwise Interactions Induced Probabilistic Model Building -- ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information -- Estimation of Distribution Algorithm Based on Copula Theory -- Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks -- Applications -- Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA -- Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics -- Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method | |
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Datensatz im Suchindex
| _version_ | 1804152238942191616 |
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| any_adam_object | |
| author | Chen, Ying-ping |
| author_facet | Chen, Ying-ping |
| author_role | aut |
| author_sort | Chen, Ying-ping |
| author_variant | y p c ypc |
| building | Verbundindex |
| bvnumber | BV041889724 |
| collection | ZDB-2-ENG |
| contents | Linkage and Problem Structures -- Linkage Structure and Genetic Evolutionary Algorithms -- Fragment as a Small Evidence of the Building Blocks Existence -- Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm -- DEUM – A Fully Multivariate EDA Based on Markov Networks -- Model Building and Exploiting -- Pairwise Interactions Induced Probabilistic Model Building -- ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information -- Estimation of Distribution Algorithm Based on Copula Theory -- Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks -- Applications -- Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA -- Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics -- Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method |
| ctrlnum | (OCoLC)664676951 (DE-599)BVBBV041889724 |
| dewey-full | 519 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519 |
| dewey-search | 519 |
| dewey-sort | 3519 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-3-642-12834-9 |
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| id | DE-604.BV041889724 |
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| indexdate | 2024-07-10T01:07:33Z |
| institution | BVB |
| isbn | 9783642128349 |
| language | English |
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| oclc_num | 664676951 |
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| physical | 1 Online-Ressource (265p. 30 illus. in color) |
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| series2 | Evolutionary Learning and Optimization |
| spelling | Chen, Ying-ping Verfasser aut Exploitation of Linkage Learning in Evolutionary Algorithms edited by Ying-ping Chen Berlin, Heidelberg Springer Berlin Heidelberg 2010 1 Online-Ressource (265p. 30 illus. in color) txt rdacontent c rdamedia cr rdacarrier Evolutionary Learning and Optimization 3 One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues Linkage and Problem Structures -- Linkage Structure and Genetic Evolutionary Algorithms -- Fragment as a Small Evidence of the Building Blocks Existence -- Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm -- DEUM – A Fully Multivariate EDA Based on Markov Networks -- Model Building and Exploiting -- Pairwise Interactions Induced Probabilistic Model Building -- ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information -- Estimation of Distribution Algorithm Based on Copula Theory -- Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks -- Applications -- Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA -- Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics -- Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method Engineering Artificial intelligence Mathematics Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Applications of Mathematics Ingenieurwissenschaften Künstliche Intelligenz Mathematik Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s Maschinelles Lernen (DE-588)4193754-5 s 1\p DE-604 Erscheint auch als Druckausgabe 978-3-642-12833-2 https://doi.org/10.1007/978-3-642-12834-9 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Chen, Ying-ping Exploitation of Linkage Learning in Evolutionary Algorithms Linkage and Problem Structures -- Linkage Structure and Genetic Evolutionary Algorithms -- Fragment as a Small Evidence of the Building Blocks Existence -- Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm -- DEUM – A Fully Multivariate EDA Based on Markov Networks -- Model Building and Exploiting -- Pairwise Interactions Induced Probabilistic Model Building -- ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information -- Estimation of Distribution Algorithm Based on Copula Theory -- Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks -- Applications -- Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA -- Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics -- Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method Engineering Artificial intelligence Mathematics Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Applications of Mathematics Ingenieurwissenschaften Künstliche Intelligenz Mathematik Maschinelles Lernen (DE-588)4193754-5 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4366912-8 |
| title | Exploitation of Linkage Learning in Evolutionary Algorithms |
| title_auth | Exploitation of Linkage Learning in Evolutionary Algorithms |
| title_exact_search | Exploitation of Linkage Learning in Evolutionary Algorithms |
| title_full | Exploitation of Linkage Learning in Evolutionary Algorithms edited by Ying-ping Chen |
| title_fullStr | Exploitation of Linkage Learning in Evolutionary Algorithms edited by Ying-ping Chen |
| title_full_unstemmed | Exploitation of Linkage Learning in Evolutionary Algorithms edited by Ying-ping Chen |
| title_short | Exploitation of Linkage Learning in Evolutionary Algorithms |
| title_sort | exploitation of linkage learning in evolutionary algorithms |
| topic | Engineering Artificial intelligence Mathematics Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Applications of Mathematics Ingenieurwissenschaften Künstliche Intelligenz Mathematik Maschinelles Lernen (DE-588)4193754-5 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Engineering Artificial intelligence Mathematics Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Applications of Mathematics Ingenieurwissenschaften Künstliche Intelligenz Mathematik Maschinelles Lernen Evolutionärer Algorithmus |
| url | https://doi.org/10.1007/978-3-642-12834-9 |
| work_keys_str_mv | AT chenyingping exploitationoflinkagelearninginevolutionaryalgorithms |