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Architecture-of-Genetic-Algorithm

Genetic Algorithm Using NS2

A genetic algorithm  is a programming technique which mimics biological evolution and used for problem-solving strategy. We have guided more than 50+ Genetic Algorithm Projects using NS2 Simulation for academic students.When introduced to a specific problem to solve, the input to the Genetic Algorithm is a set of potential solutions to that problem, encoded in different fashion, and a metric known as fitness function that allows each candidate to be quantitatively evaluated.

NS2 Projects

Concepts involved in genetic algorithm:

  • Task scheduling models.
  • Optimized Routing.
  • Vehicle navigation routing models.
  • Container terminal network model.

 

Scope of genetic algorithm used in network:

  • Node density (ND).
  • Reduce Delay of the packet arrival.
  • Increase Bandwidth availability.
  • Optimum Resource allocation (RA).
  • To achieve Network lifetime (NL).

Sample code for genetic algorithm:

proc genrand {max num} {
set result [list] for {set i 0} {$i < $num} {incr i} {
lappend result [expr {int($max * rand())}] }
return $result
}
proc generation {name gen} {
puts “0eneration: $gen0
}
proc evaluate {name entity} {
global goal
set result [expr {100 * 10}] for {set i 0} {$i < 10} {incr i} {
incr result [expr {−1 * abs([lindex $goal $i] − [lindex $entity $i])}] }
return $result
}
proc seed {name entity} {
set seed [genrand 100 10] return $seed
}
proc adapt {name entity} {
return $entity
}
proc mutate {name entity} {
lset entity [genrand 10 1] [genrand 100 1] return $entity
}
proc crossover {name entity1 entity2} {
set cross [genrand 10 1] set child1 [concat [lrange $entity1 0 $cross−1] [lrange $entity2 $cross end]] set child2 [concat [lrange $entity2 0 $cross−1] [lrange $entity1 $cross end]] return [list $child1 $child2] }

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NS2 Projects for MS Students

Wireless Sensor Network

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IPV4 , IPV6

Green Network

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4G , 5G

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Vanet

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