Building NeuroSearch – Intelligent Evolutionary Search Algorithm For Peer-to-Peer Environment Master’s Thesis by Joni Töyrylä 3.9.2004. Mikko Vapa, researcher student InBCT 3.2 Cheese Factory / P2P Communication Agora Center http:// tisu .it.jyu.fi/ cheesefactory. Contents.
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Building NeuroSearch – Intelligent Evolutionary Search Algorithm For Peer-to-Peer EnvironmentMaster’s Thesis by Joni Töyrylä 3.9.2004
Mikko Vapa, researcher studentInBCT 3.2 Cheese Factory / P2P Communication
Forward the query
Forward the query
Create candidate algorithmsrandomly
Select the bestones for nextgeneration
Breed a newpopulation
Define the quality
requirementsfor the algorithm
Finally select thebest algorithm forthese conditions
Fitness = 50 * replies – packets = 50*239 – 1290 = 10660
Note: Because of bug Steiner tree does not locate half of replies and thus gets a lower fitness than HDS
Using Hops we can forexample design rules:
”I have travelled 4 hops,I will not send further”
”Target node contains 10 neighbors,I will send further”
”Target node contains the most number ofneighbors compared to all my neighbors,I will not send further”
”I have 7 neighbors,I will send further”
”I have received this query earlier,I will not send further”
The results indicate that using only one topological information is more efficient than combining it with other topological information (the explanation for this behavior is still unclear)
Also the results indicate that using only one query related information is more efficient than combining it with other query related information (the explanation for this behavior is also unclear)
Note: Breadth-FirstSearch curve needsto be halved becausethe percentage wascalculated to half ofresources and not allavailable resources
20:10 had the greatest average hops value
What happens if the number of neuronson 2nd hidden layer is increased? Willthe average number of hops decrease?
25:10 had the greatest fitness value
Would more generations than 100.000
increase the fitness when 1st hiddenlayer contains more than 25 neurons?