Advances in Bio-inspired Computing for Combinatorial Optimization Problems

"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems. Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pintea, Camelia-Mihaela (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:Intelligent Systems Reference Library, 57
Temas:
Computational intelligence.
Artificial intelligence.
Operations research.
Computational Intelligence.
Artificial Intelligence.
Operations Research and Decision Theory.
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-3-642-40179-4
003 DE-He213
005 20220120001624.0
007 cr nn 008mamaa
008 130807s2014 gw | s |||| 0|eng d
020 |a 9783642401794  |9 978-3-642-40179-4 
024 7 |a 10.1007/978-3-642-40179-4  |2 doi 
050 4 |a Q342 
072 7 |a UYQ  |2 bicssc 
072 7 |a TEC009000  |2 bisacsh 
072 7 |a UYQ  |2 thema 
082 0 4 |a 006.3  |2 23 
100 1 |a Pintea, Camelia-Mihaela.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Advances in Bio-inspired Computing for Combinatorial Optimization Problems  |h [electronic resource] /  |c by Camelia-Mihaela Pintea. 
250 |a 1st ed. 2014. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2014. 
300 |a X, 188 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Intelligent Systems Reference Library,  |x 1868-4408 ;  |v 57 
505 0 |a Part I Biological Computing and Optimization -- Part II Ant Algorithms -- Part III Bio-inspired Multi-Agent Systems -- Part IV Applications with Bio-inspired Algorithms -- Part V Conclusions and Remarks. 
520 |a "Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems. Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed. Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents. This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems. 
650 0 |a Computational intelligence. 
650 0 |a Artificial intelligence. 
650 0 |a Operations research. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence. 
650 2 4 |a Operations Research and Decision Theory. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783642438776 
776 0 8 |i Printed edition:  |z 9783642401800 
776 0 8 |i Printed edition:  |z 9783642401787 
830 0 |a Intelligent Systems Reference Library,  |x 1868-4408 ;  |v 57 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-642-40179-4  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712) 

Ejemplares similares