Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer US
2002
|
| Ausgabe: | 1st ed. 2002 |
| Schriftenreihe: | Genetic Algorithms and Evolutionary Computation
2 |
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. '... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana |
| Beschreibung: | 1 Online-Ressource (XXXIV, 382 p) |
| ISBN: | 9781461515395 |
| DOI: | 10.1007/978-1-4615-1539-5 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV047064781 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 201216s2002 |||| o||u| ||||||eng d | ||
| 020 | |a 9781461515395 |9 978-1-4615-1539-5 | ||
| 024 | 7 | |a 10.1007/978-1-4615-1539-5 |2 doi | |
| 035 | |a (ZDB-2-SCS)978-1-4615-1539-5 | ||
| 035 | |a (OCoLC)1227484256 | ||
| 035 | |a (DE-599)BVBBV047064781 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-706 | ||
| 082 | 0 | |a 005.1 |2 23 | |
| 084 | |a ST 285 |0 (DE-625)143648: |2 rvk | ||
| 084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
| 245 | 1 | 0 | |a Estimation of Distribution Algorithms |b A New Tool for Evolutionary Computation |c edited by Pedro Larrañaga, José A. Lozano |
| 250 | |a 1st ed. 2002 | ||
| 264 | 1 | |a New York, NY |b Springer US |c 2002 | |
| 300 | |a 1 Online-Ressource (XXXIV, 382 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Genetic Algorithms and Evolutionary Computation |v 2 | |
| 520 | |a Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. | ||
| 520 | |a In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. | ||
| 520 | |a Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. '... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana | ||
| 650 | 4 | |a Software Engineering/Programming and Operating Systems | |
| 650 | 4 | |a Artificial Intelligence | |
| 650 | 4 | |a Programming Languages, Compilers, Interpreters | |
| 650 | 4 | |a Software engineering | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Programming languages (Electronic computers) | |
| 650 | 0 | 7 | |a Verteilter Algorithmus |0 (DE-588)4200453-6 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Verteilter Algorithmus |0 (DE-588)4200453-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Larrañaga, Pedro |4 edt | |
| 700 | 1 | |a Lozano, José A. |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9780792374664 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781461356042 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781461515401 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4615-1539-5 |x Verlag |z URL des Eerstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-SCS | ||
| 940 | 1 | |q ZDB-2-SCS_2000/2004 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032471892 | ||
| 966 | e | |u https://doi.org/10.1007/978-1-4615-1539-5 |l UBY01 |p ZDB-2-SCS |q ZDB-2-SCS_2000/2004 |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804182063252766720 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author2 | Larrañaga, Pedro Lozano, José A. |
| author2_role | edt edt |
| author2_variant | p l pl j a l ja jal |
| author_facet | Larrañaga, Pedro Lozano, José A. |
| building | Verbundindex |
| bvnumber | BV047064781 |
| classification_rvk | ST 285 ST 301 |
| collection | ZDB-2-SCS |
| ctrlnum | (ZDB-2-SCS)978-1-4615-1539-5 (OCoLC)1227484256 (DE-599)BVBBV047064781 |
| dewey-full | 005.1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.1 |
| dewey-search | 005.1 |
| dewey-sort | 15.1 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.1007/978-1-4615-1539-5 |
| edition | 1st ed. 2002 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04467nmm a2200589zcb4500</leader><controlfield tag="001">BV047064781</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">201216s2002 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781461515395</subfield><subfield code="9">978-1-4615-1539-5</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4615-1539-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-SCS)978-1-4615-1539-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1227484256</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047064781</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-706</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.1</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 285</subfield><subfield code="0">(DE-625)143648:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 301</subfield><subfield code="0">(DE-625)143651:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of Distribution Algorithms</subfield><subfield code="b">A New Tool for Evolutionary Computation</subfield><subfield code="c">edited by Pedro Larrañaga, José A. Lozano</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed. 2002</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer US</subfield><subfield code="c">2002</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XXXIV, 382 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Genetic Algorithms and Evolutionary Computation</subfield><subfield code="v">2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. '... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software Engineering/Programming and Operating Systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Programming Languages, Compilers, Interpreters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Programming languages (Electronic computers)</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Verteilter Algorithmus</subfield><subfield code="0">(DE-588)4200453-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Verteilter Algorithmus</subfield><subfield code="0">(DE-588)4200453-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Larrañaga, Pedro</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lozano, José A.</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9780792374664</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781461356042</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781461515401</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4615-1539-5</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Eerstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SCS</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SCS_2000/2004</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032471892</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-1-4615-1539-5</subfield><subfield code="l">UBY01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="q">ZDB-2-SCS_2000/2004</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047064781 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:12:23Z |
| indexdate | 2024-07-10T09:01:35Z |
| institution | BVB |
| isbn | 9781461515395 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032471892 |
| oclc_num | 1227484256 |
| open_access_boolean | |
| owner | DE-706 |
| owner_facet | DE-706 |
| physical | 1 Online-Ressource (XXXIV, 382 p) |
| psigel | ZDB-2-SCS ZDB-2-SCS_2000/2004 ZDB-2-SCS ZDB-2-SCS_2000/2004 |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Springer US |
| record_format | marc |
| series2 | Genetic Algorithms and Evolutionary Computation |
| spelling | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation edited by Pedro Larrañaga, José A. Lozano 1st ed. 2002 New York, NY Springer US 2002 1 Online-Ressource (XXXIV, 382 p) txt rdacontent c rdamedia cr rdacarrier Genetic Algorithms and Evolutionary Computation 2 Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. '... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana Software Engineering/Programming and Operating Systems Artificial Intelligence Programming Languages, Compilers, Interpreters Software engineering Artificial intelligence Programming languages (Electronic computers) Verteilter Algorithmus (DE-588)4200453-6 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Verteilter Algorithmus (DE-588)4200453-6 s DE-604 Larrañaga, Pedro edt Lozano, José A. edt Erscheint auch als Druck-Ausgabe 9780792374664 Erscheint auch als Druck-Ausgabe 9781461356042 Erscheint auch als Druck-Ausgabe 9781461515401 https://doi.org/10.1007/978-1-4615-1539-5 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation Software Engineering/Programming and Operating Systems Artificial Intelligence Programming Languages, Compilers, Interpreters Software engineering Artificial intelligence Programming languages (Electronic computers) Verteilter Algorithmus (DE-588)4200453-6 gnd |
| subject_GND | (DE-588)4200453-6 (DE-588)4143413-4 |
| title | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation |
| title_auth | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation |
| title_exact_search | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation |
| title_exact_search_txtP | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation |
| title_full | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation edited by Pedro Larrañaga, José A. Lozano |
| title_fullStr | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation edited by Pedro Larrañaga, José A. Lozano |
| title_full_unstemmed | Estimation of Distribution Algorithms A New Tool for Evolutionary Computation edited by Pedro Larrañaga, José A. Lozano |
| title_short | Estimation of Distribution Algorithms |
| title_sort | estimation of distribution algorithms a new tool for evolutionary computation |
| title_sub | A New Tool for Evolutionary Computation |
| topic | Software Engineering/Programming and Operating Systems Artificial Intelligence Programming Languages, Compilers, Interpreters Software engineering Artificial intelligence Programming languages (Electronic computers) Verteilter Algorithmus (DE-588)4200453-6 gnd |
| topic_facet | Software Engineering/Programming and Operating Systems Artificial Intelligence Programming Languages, Compilers, Interpreters Software engineering Artificial intelligence Programming languages (Electronic computers) Verteilter Algorithmus Aufsatzsammlung |
| url | https://doi.org/10.1007/978-1-4615-1539-5 |
| work_keys_str_mv | AT larranagapedro estimationofdistributionalgorithmsanewtoolforevolutionarycomputation AT lozanojosea estimationofdistributionalgorithmsanewtoolforevolutionarycomputation |