Evolutionary Computing Performance via MapReduce Parallel Processing
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Evolutionary Computing Performance via MapReduce Parallel Processing (2016)
DE PB NW RP
ISBN: 9783659847691 bzw. 3659847690, in Deutsch, LAP Lambert Academic Publishing Mrz 2016, Taschenbuch, neu, Nachdruck.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Neuware - Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of 'map and reduce'. The performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework. 164 pp. Englisch.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Neuware - Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of 'map and reduce'. The performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework. 164 pp. Englisch.
2
Evolutionary Computing Performance via MapReduce Parallel Processing
DE NW
ISBN: 9783659847691 bzw. 3659847690, in Deutsch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Lieferzeit: 11 Tage, zzgl. Versandkosten.
Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of "map and reduce". The performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework.
Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of "map and reduce". The performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework.
3
Evolutionary Computing Performance via MapReduce Parallel Processing
DE PB NW
ISBN: 9783659847691 bzw. 3659847690, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Evolutionary Computing Performance via MapReduce Parallel Processing: Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of `map and reduce`. The performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework. Englisch, Taschenbuch.
Evolutionary Computing Performance via MapReduce Parallel Processing: Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of `map and reduce`. The performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework. Englisch, Taschenbuch.
4
Symbolbild
Evolutionary Computing Performance via MapReduce Parallel Processing (2016)
DE PB NW RP
ISBN: 9783659847691 bzw. 3659847690, in Deutsch, Lap Lambert Academic Publishing, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, English-Book-Service Mannheim [1048135], Mannheim, Germany.
This item is printed on demand for shipment within 3 working days.
This item is printed on demand for shipment within 3 working days.
5
Evolutionary Computing Performance via MapReduce Parallel Processing
~EN PB NW
ISBN: 3659847690 bzw. 9783659847691, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
6
Evolutionary Computing Performance via MapR (2016)
~EN PB NW
ISBN: 9783659847691 bzw. 3659847690, vermutlich in Englisch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
7
Evolutionary Computing Per (2016)
DE PB NW
ISBN: 9783659847691 bzw. 3659847690, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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