Genetic Algorithm using NS2

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Genetic Algorithm using NS2 is a programming technique which mimics biological evolution and used for problem-solving strategy. We have guided more than 50+ Genetic Algorithm using NS2 Simulation Projects 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.

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Concepts involved in genetic algorithm:

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

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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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