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9
The Genetic Adaptive Routing
Algorithm
Masaharu Munetomo
9.1 Introduction
A routing algorithm constructs routing tables to forward communication packets based on
network status information. Rapid inflation of the Internet increases demand for scalable
and adaptive network routing algorithms. Conventional protocols such as the Routing
Information Protocol (RIP) (Hedrick, 1988) and the Open Shortest-Path First protocol
(OSPF) (Comer, 1995) are not adaptive algorithms; they because they only rely on hop
count metrics to calculate shortest paths. In large networks, it is difficult to realize an
adaptive algorithm based on conventional approaches. This is because they employ
broadcasts to collect information on routing tables or network link status, which causes
excessive overheads in adaptive algorithms.
In this chapter, we describee an adaptive routing algorithm called the Genetic Adaptive
Routing Algorithm (GARA). The algorithm maintains a limited number of alternative
routes that are frequently used. Instead of broadcasting a whole routing table or link status,
it only observes communication latency for the alternative routes. It also tries to balance
link loads by distributing packets among the alternative routes in its routing table. The
alternative routes are generated by path genetic operators.
9.2 Routing algorithms in the Internet
From the early history of the Internet, vector distance routing algorithms based on Bellman-
Ford’s algorithm (Bellman, 1957; Ford and Fulkerson, 1962) have usually been employed.
Telecommunications Optimization: Heuristic and Adaptive Techniques, edited by D.W. Corne, M.J. Oates and G.D. Smith
© 2000 John Wiley & Sons, Ltd
Telecommunications Optimization: Heuristic and Adaptive Techniques.
Edited by David W. Corne, Martin J. Oates, George D. Smith
Copyright © 2000 John Wiley & Sons Ltd
ISBNs: 0-471-98855-3 (Hardback); 0-470-84163X (Electronic)
Telecommunications Optimization: Heuristic and Adaptive Techniques
152

The Routing Information Protocol (RIP) (Hedrick, 1988) based on the vector-distance
algorithm with hop count metric is commonly used even now in small local area networks.
The algorithm yields many communication overheads in larger networks because they
periodically send broadcast messages that contain a whole routing table. More precisely,
the number of messages to broadcast routing tables is proportional to
2
n (n is the number
of nodes in a network) and the size of a routing table is proportional to n, which means that
the total communication overhead for the routing information exchange becomes
).(
3
nO
The vector distance algorithm also suffers from its slow convergence because it is
necessary for the routing tables to reach all the nodes in the network to obtain the correct
distance.
To reduce communication overhead, routing algorithms based on link status information
such as the SPF (Shortest Path First protocol) send broadcast messages that only contain
information on link status. Based on a topological database generated from the collected
link status, the algorithm calculates the shortest paths employing Dijkstra’s shortest path
algorithm (Dijkstra, 1959) in each node. The SPF broadcasts messages that only contain the
nodes’ link status instead of the entire routing table. Therefore, the size of a message that
contains link status information becomes
)1(O when the degree of nodes is constant such
as in a mesh network, and the size becomes
)(log nO in a hypercube network. However,
the number of messages is also proportional to
,
2
n which leads to extensive overheads in
larger size network. Therefore, the OSPF (Open Shortest Path First protocol) (Moy, 1989),

a widely used network routing protocol for Interior Gateway Protocol (IGP) (Comer, 1995)
based on the SPF, relies on hop count metric and detects topological changes such as link
failure.
It seems easy to collect information on the communication latency of links and calculate
routes with minimum delay; however, it is almost impossible in large networks. This is
because we need to collect the communication latency of all the links frequently by
broadcast messages, which leads to extremely heavy communication overheads. In
addition, delayed information for the latency may create far from optimal routes. In a huge
network such as the Internet, it is essentially important for routing algorithms to be
scalable. To achieve scalability in adaptive network routing algorithms, it is necessary to
observe communication latency with as few communication overheads as possible.
9.3 GAs in Network Resource Management
Before introducing our routing algorithm by GA, this section gives a brief review of related
work concerning the application of GAs to network resource management problems. Since
many of the problems are classified into combinatorial optimization problems, they can
directly be solved by GAs. For example, an application of GA to network routing in
telecommunication networks is proposed in the Handbook of Genetic Algorithms (Davis,
1991) by Cox et al. (1991). The authors solved a constrained optimization problem that
allocates link bandwidth to each connection. For another approach, Munakata and Hashier
(1993) solved the maximum network flow problem. The objective of this optimization
problem is to maximize the network flow based on the global information of the network.
These two algorithms cannot be applied directly to routing in packet-switching networks
such as the Internet because they are based on circuit-switching networks, in which the
The Genetic Adaptive Routing Algorithm
153
bandwidth of the network is allocated to circuits – connections between nodes (Tanenbaum,
1988). On the other hand, Carse et al. (1995; and Chapter 8) applied a Fuzzy Classifier
System (FCS) to a distributed routing algorithm on packet-switching networks in which
each packet is routed independently by a routing table. This routing algorithm, however,
only decides whether a packet should take a direct route or an indirect route by using a

FCS. Therefore, the algorithm cannot be directly applied to actual routing algorithms.
9.4 Overview of the GARA
The Genetic Adaptive Routing Algorithm (GARA) is an adaptive routing algorithm that
employs genetic operators to create alternative routes in a routing table. It is based on
source routing algorithms, which determine the entire route of a packet in its source node.
Each node has a routing table containing a set of alternative routes, each of which is created
by genetic operators. Each packet selects one of the alternative routes to the destination.
The algorithm observes the communication latency of routes frequently used.
Figure 9.1 Overview of the GARA.
Figure 9.1 shows an overview of the GARA. Each node has a routing table that contains
routes to destinations. For each destination, a set of alternative routes is assigned. Each
route has its weight value (as its fitness) that specifies the probability for the route to be
selected from a set of alternative routes. The weight is calculated from the communication
latency, observed by sending delay query packets. At the beginning, the routing table is
empty. When a packet must be sent to a destination, a default route to the destination is
generated by using Dijkstra’s shortest path algorithm based on hop count metric. After a
specified number of packets are sent along a route, a packet is sent to observe the
Node
Routing table
Dest. Route Delay Weight
0
1
2
3
4
5
6
1 (0 1) 100 1.0
4 (0 6 4) 70 0.7
(0 3 4) 90 0.3

5 (0 3 5) 100 0.7
(0 3 4 5) 150 0.1
(0 6 4 5) 130 0.2
6 (0 6) 50 1.0
Delay
Query &
Answer
Applying
Path Genetic Operators
(Only contains routes frequently used)
Network
Telecommunications Optimization: Heuristic and Adaptive Techniques
154
communication latency of the route. To generate alternative routes, path genetic operators
are invoked at a specified probability after every evaluation of weight values. To prevent
overflow of a routing table, selection operators are applied to reduce its size. A selection
operator deletes a route that has the lowest weight among the routes to a destination.
Moreover, another selection operator may be applied; this deletes all the routes to a
destination to which the number of packets sent is the smallest among all the destinations in
a routing table.
The GARA can greatly reduce communication overheads for dynamic observation of
network status by limiting the observation to alternative routes frequently used. It can also
reduce the possibility of congestion by distributing packets probabilistically among the
alternative routes.
9.5 Path Genetic Operators
For the GARA, path genetic operators such as path mutation and path crossover are
designed to generate alternative routes based on the topological information of the network.
The path mutation operator causes a perturbation of a route in order to create an alternative
route. The path crossover exchanges sub-routes between a pair of routes.
A route (path) is encoded into a list of node IDs from its source node to its destination.

For example, a route from node 0 to node 9 is encoded into a list of nodes along the route:
(0 12 5 8 2 9). The route is constrained to the network topology; that is, each step of a route
must pass through a physical link of the network.
9.5.1 Path Mutation
The path mutation generates an alternative route by means of a perturbation. Figure 9.2
shows how to perform this mutation operator. To perform a mutation, first, a node is
selected randomly from the original route, which is called a mutation node. Secondly,
another node is randomly selected from nodes directly connected to the mutation node.
Thirdly, an alternative route is generated by connecting the source node to the selected node
and the selected node to the destination employing Dijkstra’s shortest path algorithm.
More precisely, the path mutation proceeds according to the following sequence where r
is the original route and r
′ is its mutated one.
1. A mutation node
m
n is selected randomly from all nodes along the route r, except its
source and destination nodes.
2. Another node
m
n ′ is selected randomly from neighbors of the mutation node, i.e.
m
n ′ ).(
m
n
ε
∈
3. It generates a shortest path
1
r from the source node to
m

n ′, and another shortest path
2
r from
m
n ′ to the destination.
4. If there is no duplication of nodes between
1
r and
2
r , they are connected to have a
mutated route
.
21
rrr += Otherwise, they are discarded and no mutation is performed.
The Genetic Adaptive Routing Algorithm
155
Figure 9.2 Path mutation applied to a route.
Suppose that the mutation operator is applied to a route r = (0 3 5 6 7 10 12 15). First, we
select, for example, a node 7 as a mutation node. Secondly, another node 8, for example, is
randomly selected from the neighbors of the mutation node. Thirdly, by Dijkstra’s shortest
path algorithm, we connect the source node 0 and the selected node 8 to generate a path
1
r =(0 2 4 8). We also connect node 8 and the destination node 15 to generate another path
2
r = (8 10 12 15). We finish the mutation by connecting the routes
1
r and
2
r to eventually
have r′ = (0 2 4 8

10 12 15). We do not generate a route r′ if any duplication exists between
1
r and .
2
r This is because we need to avoid creating any loop in a route that passes through
the same nodes twice or more.
9.5.2 Path crossover
The path crossover exchanges sub-routes between a pair of routes. To perform the
crossover, the pair should have the same source node and destination node. A crossing site
of the path crossover operator is selected from nodes contained in both routes. The
crossover exchanges sub-routes after the selected crossing site. Figure 9.3 shows an
overview of the operator.
The crossover operator applied to a pair of routes
1
r and
2
r proceeds as the following
sequence.
1. A set of nodes
c
N included in both
1
r and
2
r (excluding source and destination nodes)
are selected as potential crossing sites. If
c
N is an empty set, we do not perform the
crossover.
2. A node

c
n is selected randomly from
c
N as a crossing site.
3. The crossover is performed by exchanging all nodes after the crossing site
c
n between
1
r and .
2
r
Suppose that a path crossover is applied to a pair of routes
1
r and
2
r from node 0 to node
20 as in the following:
Original Path
Mutation
Mutated Path
r1
r2
n’
m
n
m
Telecommunications Optimization: Heuristic and Adaptive Techniques
156
Figure 9.3 Path crossover applied to a pair of routes.
1

r = (0 2 3 7 9 11 12 15 17 18 20),
2
r = (0 4 5 7 10 11 13 15 16 20).
Their potential crossing sites are nodes 7, 11 and 15. When we select, for example, the node
11 as a crossing site, the offspring are generated by exchanging the sub-routes after the
crossing site:
1
r = (0 2 3 7 9 11 13 15 16 20),
2
r = (0 4 5 7 10 11 12 15 17 18 20).
When we do not have a common node in a pair of routes, we cannot select a crossing site
and we do not perform a crossover. In our earlier work such as Amano et al. (1993) and
Murai et al. (1996), in order to perform a crossover even when the pair has no common
nodes, we connect crossing sites by calculating the shortest path between them. However, a
pair of routes without any similarity is not worth crossing-over, because the operator may
generate routes which are too far from their parents for such routes, which may lead to
random regenerations of routes.
Route r1
Route r2
Crossing Site
Crossover
Crossing Site
Route r1 (Changed)
Route r2 (Changed)
Potential crossing site
Selected crossing site
The Genetic Adaptive Routing Algorithm
157
9.6 Maintaining Routing Tables
Table 9.1 shows a routing table used in the GARA. The routing table consists of five entries

named destination, route, frequency, delay and weight. For each destination, we have a set
of alternative routes. The frequency of a route specifies the number of packets sent to the
destination along the route. The delay entry stores the communication latency of packets
sent along the route. The weight of a route is calculated by its communication latency,
which specifies the probability for the route to be selected when a packet is sent. From a
GA’s point of view, the weight corresponds to a fitness value.
Table 9.1 A routing table.
Destination Route Frequency Delay Weight
2 (1 3 2)*
(1 3 4 2)
(1 3 4 5 2)
7232
2254
1039
50
60
70
0.7
0.2
0.1
6 (1 8 6)*
(1 10 11 6)
20983
34981
100
105
0.4
0.6
8 (1 8)*
(1 7 8)

30452
3083
40
40
0.9
0.1
In the table, routes marked with asterisks are the default routes which are the shortest
paths based on hop-count metric. Initially, the routing table is empty. When it becomes
necessary to send a packet to a destination, and if there is no route to the destination, a
default route is generated by Dijkstra’s algorithm and is inserted to the routing table. After
sending a specified number of packets along a route, we send a packet that observes the
communication latency of the route. The fitness value is calculated after receiving its
answer. After the observation, path genetic operators are applied to the routes at a specified
probability to generate alternative routes.
9.7 Fitness Evaluation
A weight value (fitness value) of a route specifies the probability for the route to be
selected from alternative routes. It is calculated from the communication latency along the
routes. To observe delay along the route, a delay query message is issued at a specified
interval. Using the delay obtained, we calculate weight values
i
w using the following
equations:
∑∑
∈∈
==
Sj
j
i
i
Sj

j
i
i
d
d
w
η
η
η
where,
/1
/1
(9.1)
Telecommunications Optimization: Heuristic and Adaptive Techniques
158
where
i
d is the delay for route i and S is a set of routes to the same destination. In the
above equations, we first normalize the delay values
i
d by dividing by their total sum to
yield
.
i
η
Second, we calculate the reciprocal number of
i
η
and normalize them to have a
weight value. This is because we need to have a larger weight of

i
w for a smaller delay of
.
i
d Consequently, a route with smaller delay is frequently employed in sending packets.
However, note that the selection of routes is a randomized one; routes with longer delay
also have a chance to be selected.
9.8 Execution Flow
In the GARA, each node executes the same algorithm independently. Figure 9.4 shows a
pseudo PASCAL code of the algorithm. Each packet has entries such as type, route and
next, where type specifies type of a packet, route entry is the route of the packet, and
next indicates next hop in its route. Types of packets are DataPacket (which contains
data), DelayRequest (for requesting delay of a link), and DelayAnswer (for
answering a DelayRequest packet). DelayRequst and DelayAnswer packets have
DelayEntry which records the communication latency of the packets.
Every time a packet is created at a node, the node determines a route for the packet
based on its routing table. For a packet arriving from another node, if its type is
DataPacket, the node simply forwards the packet according to its route. In the initial
state, a routing table is empty. If the routing table does not contain a route for the
destination of a packet created, a default route is generated by employing Dijkstra’s shortest
path algorithm and is inserted to the table.
Each time after a specified number of packets are sent along a route, a
DelayRequest packet is sent to observe the communication latency along the route. If
the packet arrives at the destination, a
DelayAnswer packet is sent back. After receiving
the answer, the communication latency of the route is obtained by calculating the average
amount of time sending a
DelayRequest packet and receiving a DelayAnswer packet.
After obtaining the delay of a route, weight values of routes are calculated according to
equation 9.1. After every evaluation of weights, genetic operators are invoked at a specified

probability to create alternative routes in the routing table.
If the size of the routing table exceeds a limit, we perform a selection to reduce its size.
We have two types of selection operators: local selection is invoked if the number of
strings exceeds a limit, and it deletes a route with the smallest weight among routes with
the same destination; and global selection is invoked when the number of destinations in
the routing table exceeds a limit, and it deletes all the routes to a destination of which
frequencies of sending packets is the smallest among all destinations in the routing table.
9.9 Empirical Results
To evaluate routing algorithms, it is important to perform experiments for networks large
enough. In the following experiments, also discussed in Munetomo et al. (1998a), a sample
network with 20 nodes is employed (Figure 9.5). The bandwidth of a link is represented by
its thickness. The thicker link is for the link with 4.5 Mbps, and the thinner one has
bandwidth of 1.5 Mbps.
The Genetic Adaptive Routing Algorithm
159
begin
Initialize routing table;
while not terminated do
begin
wait for a packet to be received or input from user;
if packet.type = DataPacket then
begin
if the packet is sent from other node then
begin
if packet.destination = this node ID then receive packet;
else send the packet to the node of packet.next;
end
else (* if the packet is input from user at the node *)
begin
if routing table for the destination is empty then

begin
create a default route by using Dijkstra’s algorithm;
add the default route to the routing table;
reset frequency entry of the default route;
packet.route := default route;
if the number of destination > limit then
delete routes of a destination least frequently used;
end
else
begin
select a route from the routing table by roulette wheel selection;
increment frequency entry of the selected route;
packet.route := the selected route;
if route.frequency mod EvaluationInterval = 0 then
begin
packet.type := DelayRequest;
packet.route := route;
send the packet according to the route;
end
end
end
end
if packet.type = DelayRequest then
begin
packet.DelayEntry := CurrentTime – packet.CreateTime;
packet.CreateTime := CurrentTime;
packet.type := DelayAnswer;
send packet to its source;
end;
if packet.type = DelayAnswer then

begin
packet.DelayEntry := packet.DelayEntry + CurrentTime – packet.CreateTime;
packet.delay_entry := packet.DelayEntry/2;
change delay of the route based on packet.DelayEntry;
if random < Pmutation then
begin
apply a mutation to a route in the routing table;
add the string created to the routing table;
if table_size > limit then delete a string of the lowest weight;
end
if random < Pcrossover then
begin
apply a crossover to a pair of route in the routing table;
add the string created to the routing table;
if table_size > limit then delete a string of the lowest weight;
end
end;
end
end.
Figure 9.4 Pseudo PASCAL code for the GARA.
Telecommunications Optimization: Heuristic and Adaptive Techniques
160
Figure 9.5 A sample network for the experiment.
We compare the GARA with conventional routing algorithms under the following
conditions: at each node in the network, data packets are randomly generated at a specified
interval that is exponentially distributed. The destination of a packet is randomly selected
from nodes that are represented by circles with gray inside (see Figure 9.5). Delay query
packets are sent every after 10 data packets are sent. The probability to apply a mutation
after an evaluation is 0.1 and that to apply a crossover is 0.05. The maximum population
size is 100. We continue the simulation for 3000(s) to obtain the following results.

In Figure 9.6, we compare the mean arrival time of data packets for the RIP, for the
SPF, for an adaptive version of the SPF, and for the GARA. The adaptive SPF observes
communication latency of links to calculate the shortest paths (we set the delay observation
interval at 30(s) and 60(s) for the experiments). The mean arrival time indicates the mean
value of the time it took to arrive at destinations of packets from their creation on the
source nodes. We can change the frequency of generating data packets by changing the
mean generation interval of data packets which is exponentially distributed.
This figure shows that the mean arrival time of packets is smallest when we employ the
GARA algorithm for routing. The SPF is slightly better than the RIP. Adaptive SPFs suffer
from communication overhead caused by their frequent observation of the link status of all
links, especially when the network is lightly loaded (longer mean generation interval). For a
2000 (ms) interval of creating packets, which leads to heavily loaded links in the network,
the GARA achieves about 20% of the mean arrival time compared with those of the RIP
and the SPF. This means that packets sent by the GARA arrived at their destinations five
times faster than those sent by the other algorithms. In this experiment, the adaptive SPF
0
1
2
3
4
5
6
7
10
8
9
11
12
13
14

15
16
17
1819
4.5 M
1.5 M
The Genetic Adaptive Routing Algorithm
161
with a 30 (s) observation interval becomes the best in a heavily loaded situation; however,
in larger networks, the observation of delay of all links in the networks must cause a serious
communication overhead.
Figure 9.6 Mean arrival time of packets.
Figure 9.7 shows the number of packets sent by the routing algorithms in the network.
For frequent observations, the adaptive SPF needs to send a number of packets. The RIP
and the SPF send almost the same number of packets (the size of each packet is different).
To reduce the number of packets in the adaptive SPF, it is necessary to increase the
observation interval. In this experiment, the adaptive SPF with a 60s interval could reduce
1000
10000
100000
1800 2000 2200 2400 2600 2800
Mean arrival time (ms)
Mean generation interval (ms)
GARA
adaptive SPF (30s)
adaptive SPF (60s)
SPF
RIP
Telecommunications Optimization: Heuristic and Adaptive Techniques
162

the number to nearly the same as the RIP and the SPF. However, less frequent observation
causes inaccurate observed latency of links, which may not generate the shortest paths. This
degrades total arrival time as shown in Figure 9.6. On the other hand, the GARA achieves a
smaller arrival time with much lower communication overheads. The number of packets
sent becomes less than 20% of those sent for the RIP and the SPF.
Figure 9.7 The number of packets sent by the routing algorithms.
To see the communication overheads of the network, Figures 9.8–9.11 display load
status of links for the RIP, the SPF, the adaptive SPF and the GARA. The width of each
link represents the log-scaled mean queue length for the link.
0
10000
20000
30000
40000
50000
60000
70000
80000
1800 2000 2200 2400 2600 2800
The number of packets
Mean generation interval (ms)
GARA
adaptive SPF (30s)
adaptive SPF (60s)
SPF
RIP
The Genetic Adaptive Routing Algorithm
163
Figure 9.8 Load status of links (RIP).
In the routing information protocol, a path such as 11 ⇒ 13 ⇒ 18 becomes extremely

heavily loaded. On the other hand, a path such as 11
⇒ 12 ⇒ 15 ⇒ 16 ⇒ 17 ⇒ 18, which
is an alternative to 11
⇒ 13 ⇒ 18, is not frequently used at all; therefore, links for this
more roundabout path are lightly loaded. By employing the shortest-path first protocol, we
can slightly reduce the load on the links. For the adaptive shortest path first protocol, the
majority of the links become heavily loaded because of the frequently broadcast messages
to observe the communication latency of all links. On the other hand, the GARA can
greatly reduce the load of links, especially on heavily loaded ones. This is not only because
the GARA algorithm calculates paths which minimize communication latency with less
communication overhead, but also because it distributes packets among alternative paths in
the routing table which avoids heavily loaded links.
Table 9.2 shows a routing table generated in the node 0 after a simulation run under a
2200 (ms) arrival interval of creating packets. With reference to Table 9.2, we can observe
the following points. For a destination node 12, the best route in terms of delay is clearly (0
4 7 11 12). An alternative route (0 4 10 12) is the shortest path from node 0 to node 12 in
terms of the hop-count metric, but it is not the path with minimum delay because the links
from node 7 to 11 and from node 11 to 12 have more bandwidth than the other links. This
result also shows that a load balancing among alternative routes is realized by distributing
packets probabilistically based on their weight values.
0
1
2
3
4
5
6
7
10
8

9
11
12
13
14
15
16
17
1819
Telecommunications Optimization: Heuristic and Adaptive Techniques
164
0
1
2
3
4
5
6
7
10
8
9
11
12
13
14
15
16
17
1819

Figure 9.9 Load status of links (SPF).
Figure 9.10 Load status of links (adaptive SPF, 30 s interval).
0
1
2
3
4
5
6
7
10
8
9
11
12
13
14
15
16
17
1819
The Genetic Adaptive Routing Algorithm
165
Figure 9.11 Load status of links (GARA).
Table 9.2 The routing table in node 0 generated after the simulation.
Destination Route Delay Weight
3 (0 1 3)
(0 4 2 3)
554
2253

0.802636
0.197364
7 (0 4 7) 5052 1.000000
11 (0 4 7 11)
(0 1 3 7 11)
4423
5058
0.533488
0.466512
12 (0 4 7 11 12)
(0 4 10 12)
(0 1 2 4 10 12)
2941
6210
9833
0.564116
0.267160
0.168724
17 (0 4 10 14 16 17) 2 859 1.000000
0
1
2
3
4
5
6
7
10
8
9

11
12
13
14
15
16
17
1819
Telecommunications Optimization: Heuristic and Adaptive Techniques
166
9.10 Conclusions
The GARA realizes an effective adaptive routing with less communication overhead by the
following mechanisms:
• It observes the communication latency of routes frequently used.
• It generates an appropriate set of alternative routes employing genetic operators.
• It distributes packets among the alternative routes, which realizes a load balancing
mechanism among them.
Concerning communication overhead of the GARA, only O(kn) messages are necessary to
observe the load status of routes, where k is the number of data packets sent from a node
and n is the number of nodes. On the other hand, the RIP needs
)(
3
nO communication
overhead and the SPF needs at least
)(
2
nO (it depends on the network topology) for the
observation. This means that the GARA is a scalable routing algorithm which works much
better in a larger size network than the conventional routing algorithms.
A possible problem in applying the GARA to the Interior Gateway Protocols (IGPs)

will be in its source routing approach. The Internet Protocol (IP) (Black, 1995) has a source
routing option, but is not usually employed in the IGPs. Instead, it specifies the next hop for
a packet to be forwarded. Obtaining the next hop from the route in the routing table
generated by the GARA seems not so difficult, but it may cause some problems such as
loops or invalid routes that never reach their destinations. Despite the above problem, the
GARA is easily applied to Exterior Gateway Protocols (EGPs) such as the BGP4 (Border
Gateway Protocols) which employ source routing mechanisms.