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{{Veranstaltung
|Titel DE=Peer-to-peer Evolutionary Computation
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|Titel DE=Peer-to-Peer Evolutionary Computation
|Titel EN=Peer-to-peer Evolutionary Computation
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|Titel EN=Peer-to-Peer Evolutionary Computation
 
|Beschreibung DE=Evolutionary Algorithms (EAs) are a set of bio-inspired techniques able to solve optimization problems in reasonable time. However, the execution times in EAs can be high when tackling very demanding problem domains, as in e.g. design, simulation based search or large scale problem instances. It is at this point where parallelism arises as an alternative to improve the algorithm performance and to speed up times to solutions.
 
|Beschreibung DE=Evolutionary Algorithms (EAs) are a set of bio-inspired techniques able to solve optimization problems in reasonable time. However, the execution times in EAs can be high when tackling very demanding problem domains, as in e.g. design, simulation based search or large scale problem instances. It is at this point where parallelism arises as an alternative to improve the algorithm performance and to speed up times to solutions.
  
 
In that context, we present a spatially structured EA which takes full and seamless advantage of the large amount of available resources in Peer-to-Peer (P2P) platforms. Such an approach defines a decentralised population structure by means of a P2P protocol where individuals have the mating choice locally restricted within the P2P neighbourhood. The emergent population structure behaves as a small-world topology and plays an important role in the preservation of the genetic diversity. That way, population sizes can be minimized and execution times improve.
 
In that context, we present a spatially structured EA which takes full and seamless advantage of the large amount of available resources in Peer-to-Peer (P2P) platforms. Such an approach defines a decentralised population structure by means of a P2P protocol where individuals have the mating choice locally restricted within the P2P neighbourhood. The emergent population structure behaves as a small-world topology and plays an important role in the preservation of the genetic diversity. That way, population sizes can be minimized and execution times improve.
  
The talk will tackle the main challenges towards an efficient design of P2P EAs. Questions such as decentralization (such a computation paradigm is devoid of any central server), massive scalability (P2P systems are large-scale networks) or fault tolerance (given that computational resources are added and eliminated dynamically) become of the maximum interest and will be addressed.  
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The talk will tackle the main challenges towards an efficient design of P2P EAs. Questions such as decentralization (such a computation paradigm is devoid of any central server), massive scalability (P2P systems are large-scale networks) or fault tolerance (given that computational resources are added and eliminated dynamically) become of the maximum interest and will be addressed.
 
 
 
|Beschreibung EN=Evolutionary Algorithms (EAs) are a set of bio-inspired techniques able to solve optimization problems in reasonable time. However, the execution times in EAs can be high when tackling very demanding problem domains, as in e.g. design, simulation based search or large scale problem instances. It is at this point where parallelism arises as an alternative to improve the algorithm performance and to speed up times to solutions.
 
|Beschreibung EN=Evolutionary Algorithms (EAs) are a set of bio-inspired techniques able to solve optimization problems in reasonable time. However, the execution times in EAs can be high when tackling very demanding problem domains, as in e.g. design, simulation based search or large scale problem instances. It is at this point where parallelism arises as an alternative to improve the algorithm performance and to speed up times to solutions.
  
 
In that context, we present a spatially structured EA which takes full and seamless advantage of the large amount of available resources in Peer-to-Peer (P2P) platforms. Such an approach defines a decentralised population structure by means of a P2P protocol where individuals have the mating choice locally restricted within the P2P neighbourhood. The emergent population structure behaves as a small-world topology and plays an important role in the preservation of the genetic diversity. That way, population sizes can be minimized and execution times improve.
 
In that context, we present a spatially structured EA which takes full and seamless advantage of the large amount of available resources in Peer-to-Peer (P2P) platforms. Such an approach defines a decentralised population structure by means of a P2P protocol where individuals have the mating choice locally restricted within the P2P neighbourhood. The emergent population structure behaves as a small-world topology and plays an important role in the preservation of the genetic diversity. That way, population sizes can be minimized and execution times improve.
  
The talk will tackle the main challenges towards an efficient design of P2P EAs. Questions such as decentralization (such a computation paradigm is devoid of any central server), massive scalability (P2P systems are large-scale networks) or fault tolerance (given that computational resources are added and eliminated dynamically) become of the maximum interest and will be addressed.  
+
The talk will tackle the main challenges towards an efficient design of P2P EAs. Questions such as decentralization (such a computation paradigm is devoid of any central server), massive scalability (P2P systems are large-scale networks) or fault tolerance (given that computational resources are added and eliminated dynamically) become of the maximum interest and will be addressed.
 
 
 
|Veranstaltungsart=Kolloquium Angewandte Informatik
 
|Veranstaltungsart=Kolloquium Angewandte Informatik
 
|Start=2011/06/24 14:00:00
 
|Start=2011/06/24 14:00:00

Version vom 7. Juni 2011, 12:50 Uhr

Peer-to-Peer Evolutionary Computation

Veranstaltungsart:
Kolloquium Angewandte Informatik




Evolutionary Algorithms (EAs) are a set of bio-inspired techniques able to solve optimization problems in reasonable time. However, the execution times in EAs can be high when tackling very demanding problem domains, as in e.g. design, simulation based search or large scale problem instances. It is at this point where parallelism arises as an alternative to improve the algorithm performance and to speed up times to solutions.

In that context, we present a spatially structured EA which takes full and seamless advantage of the large amount of available resources in Peer-to-Peer (P2P) platforms. Such an approach defines a decentralised population structure by means of a P2P protocol where individuals have the mating choice locally restricted within the P2P neighbourhood. The emergent population structure behaves as a small-world topology and plays an important role in the preservation of the genetic diversity. That way, population sizes can be minimized and execution times improve.

The talk will tackle the main challenges towards an efficient design of P2P EAs. Questions such as decentralization (such a computation paradigm is devoid of any central server), massive scalability (P2P systems are large-scale networks) or fault tolerance (given that computational resources are added and eliminated dynamically) become of the maximum interest and will be addressed.

(Dr. Juan Luis Jimenez Laredo)




Start: 24. Juni 2011 um 14:00
Ende: 24. Januar 2011 um 15:30


Im Gebäude 11.40, Raum: 231

Veranstaltung vormerken: (iCal)


Veranstalter: Forschungsgruppe(n) Effiziente Algorithmen