A heuristic approach for optimizing travel planning using genetics algorithm

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 531
A HEURISTIC APPROACH FOR OPTIMIZING TRAVEL PLANNING
USING GENETICS ALGORITHM
Md. Lutful Islam1
, Danish Pandhare2
, Arshad Makhthedar3
, Nadeem Shaikh4
1
Assistant Professor, Computer Engineering, MHSSCOE, Maharashtra, India
2
Student, Computer Engineering, MHSSCOE, Maharashtra, India
3
4
Abstract
In today’s fast-paced society, everyone is caught up in the hustle and bustle of life which has resulted in ineffective Planning of
their very important vacation tour. Either they spend much time on deciding what to do next, or will take many unnecessary,
unfocused and inefficient steps. The main purpose of our project is to develop a Travel Planner that will allow the customer to
plan the entire tour so that he visits many places in less time. The concept would be implemented using Genetics Algorithm of
Artificial Intelligence which would be used as a search algorithm to find the nearest optimal travel path. Moreover, In order to
reduce the running time of GA, Parallelization of Genetics Algorithm would be demonstrated using Hadoop Framework.
Key Words: Genetics Algorithm, TSP, Hadoop, and MapReduce etc…
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1. INTRODUCTION
Travel Planning has become an emerging topic and has
attracted much attention in recent years. It is becoming
increasingly important to provide travellers with craving
information to assist them in the tour. Travellers, whether
leisure -tourists- or business travellers will want on top of
the attractions that a given destination offers, a flexible and
convenient transport means to reach it. So the purpose of
this Travel Planning System is to provide the traveller with
optimal, feasible and personalized route between origin and
destination [1].
The Travel Planning System is quite allied to TSP. So we
would be implementing this by ruminating the Travelling
Salesman Problem. The TSP deals with finding a route
covering all cities so that the total distance travelled is
minimal. There are mainly three reasons why TSP has
attracted the attention of many researcher‟s and remains an
active research area. First, a large number of real-world
problems can be modelled by TSP and it falls in
distinguished category of hard problems. Second, it was
proved to be NP-Complete problem and cannot be solved
exactly in polynomial time. Third, NP-Complete problems
are intractable in the sense that no one has found any really
efficient way of solving them for large problem size. Also,
NP-complete problems are known to be more or less
equivalent to each other; if one knew how to solve one of
them one could solve the lot [2].
Conventional Linear Programming methods do find Optimal
Solution to TSP but are infeasible and impracticable as the
time required to obtain the solution is very huge. Since real
time applications of TSP has higher problem complexity as
it consists of hundreds and thousands of nodes, the primary
objective of obtaining exact optimal solutions have been
shifted to obtaining heuristically good solutions.
Genetics Algorithm (GA) is one such Heuristics based
Algorithm that tends to find good and novel solutions to
TSP within reasonable time. GA‟s are relatively new
paradigms in artificial intelligence which are based on the
principles of natural selection [3]. Based on a predefined
Fitness value, GA iteratively goes on detecting the fittest
solution which increases its performance thereby making it
more suitable for finding solutions for many optimization
problems.
One of the key points of GA is its ability of being
parallelized. Thus, the Travel Planner will attempt to
minimize the runtime of Travelling Salesman Problem using
Genetics Algorithm by parallelizing it on Hadoop. Hadoop‟s
MapReduce Framework provides better scalability,
robustness, fault tolerance and easy means of access and
use.
2. BACKGROUND
Before delivering the implementation details of how to
integrate MapReduce and Genetic algorithms, discussion
regarding certain concepts is highly significant. The
following shows a brief overview of various topics.
2.1 Travelling Salesman Problem
Many mathematicians and Computer Scientists have
immensely addressed the Travelling Salesman Problem
which is considered to be the basic, fundamental and one of
the toughest problems in Computer Science and Operations
Research. The first instance of the traveling salesman
problem was from Euler in 1759 whose problem was to

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move a knight to every position on a chess board exactly
once. The traveling salesman first gained fame in a book
written by German salesman BF Voigt in 1832 on how to be
a successful traveling salesman [4]. Its importance stems
from the fact there is a plethora of fields in which it finds
potential applications such as automatic drilling of printed
circuit boards, threading of scan cells in a testable VLSI
circuit, X-ray crystallography, DNA fragments and many
more.
It can be normally defined as :-
1. Known Facts: A network of „n‟ cities named {c1, c2,
c3..., cn} with c1 as the starting node or city. A Cost Matrix
„C‟= [Cij] denoting the calculated Euclidean distances
between the cities „i‟ and „j‟.
2. Constraints: Each city should be visited exactly once.
The traveller should return to the starting city.
3. Problem: To find the least cost Hamiltonian cycle. Thus,
the aim is to obtain an optimal path cost such that the final
sum of all distances between each node and its successor is
minimized. An important point to note is that the first node
becomes the successor of the last node in order to satisfy the
constraints of the problem.
The distances in the cost matrix can be Straight Line
Distance, Euclidean distance or City Block (Manhattan)
distance. Given two cities with co-ordinates c1=(x1, y1) and
c2=(x2, y2), the Euclidean distance say „d‟ can be calculated
as:
The Travelling Salesman Problem has some variations
which can be observed from the cost matrix „C‟.TSP is said
to be Symmetric if Cij equals Cji for all values of „i‟ and „j‟.
It will be asymmetric otherwise. Thus, if a TSP problem
with n-cities is considered then there will (n-1)! Possible
solutions. Out of which the one with the minimum cost has
to be selected. This becomes extremely difficult and
impracticable as the number of possible solutions becomes
too large even for a moderate value of „n‟.
2.2 Genetics Algorithm
Genetic Algorithms (GAs) are adaptive heuristic search
algorithm premised on the evolutionary ideas of natural
selection and genetics. As such they represent an intelligent
exploitation of a random search used to solve optimization
problems. Although randomized, GAs are by no means
random, instead they exploit historical information to direct
the search into the region of better performance within the
search space. The basic techniques of the GAs are designed
to simulate processes in natural systems necessary for
evolution, specially those follow the principles first laid
down by Charles Darwin of "survival of the fittest" [5].
Genetic algorithms are based on the principle of Genetics
and Evolution which implies that the fitter individuals are
more likely to survive and have a greater chance of passing
their good genetic features to the next generation [6].
“Genetics” is derived from Greek word “genesis” meaning
„to grow‟ or „to become‟. GA was first pioneered by John
Holland in 1975 in the book “Adaptation in Natural and
Artificial System” [7].
2.2.1 Why Genetics Algorithm?
GA is used since it is better than conventional AI systems.
Unlike older AI systems, they do not break easily even if the
input changes slightly, or in the presence of reasonable
noise. Also, in searching large state space , multi-modal
state-space , or n-dimensional surface , a genetic algorithm
may offer significant benefits over more typical search of
optimization techniques.(linear programming, heuristics,
depth-first, breadth-first, and praxis) [5]. Not only does GAs
provide an alternative methods to solving problem, it
consistently outperforms other traditional methods in most
of the problems link. Many of the real world problems
involved finding optimal parameters, which might prove
difficult for traditional methods but ideal for GAs. The
appeal of GAs comes from their simplicity and elegance as
robust search algorithms as well as from their power to
discover good solutions rapidly for difficult high-
dimensional problems. GAs are useful and efficient when
the search space is large, complex, or poorly understood,
domain knowledge is scarce or expert knowledge is difficult
to encode to narrow the search space, no mathematics
analysis is available and when the traditional search
methods fail [8]. The continuing improvement in the
performance value of GA‟s has made them attractive for
many types of problem solving optimization methods. In
particular, genetic algorithms work very well on mixed
(continuous and discrete) combinatorial problems. Moreover
they are also less susceptible to getting 'stuck' at local
optima than gradient search methods[3].
2.2.2 Working
GA begins by generating an initial population which is
called as „genome‟ or a „gene pool‟ which will then be used
to generate successive generations. This is achieved by
application of three genetic operations to generate new and
better population as compared to the older generations. A
fitness score is assigned to each solution representing the
abilities of an individual to „compete‟.
The very first operator is Selection operator in which the
individual with the optimal (or generally near optimal)
fitness score is sought. Parents are selected to mate, on the
basis of their fitness, producing offspring via a reproductive
plan. Consequently highly fit solutions are given more
opportunities to reproduce , so that the offspring inherit
characteristics from each parent [5].
The second operator is Crossover wherein intermixing of
alleles of two parents are performed to obtain the new
offspring.
The third operator is Mutation that maintains uniqueness
and diversity of the gene pool in order to prevent any loss of

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genetic information caused due to crossover. The process
repeats until an optimal solution is discovered. Individuals
in the population die and are replaced by the new solutions
,eventually creating a generation once all mating
opportunities in the old population have been exhausted. In
this the better solutions thrive while the least fit solutions
die [5].
3. PROPOSED GA FOR TSP
The aim of this paper is to develop a software for proper
panning of the entire tour. This will implicitly include
solution of Travelling Salesman Problem using Genetics
Algorithm. Details of different components and operators
required are as follows:
3.1 Initialization of the Gene Pool (Population)
A random population is initially generated that consists of
some tours associated with their respective costs. The
individuals in the pool should be different from each other
in order to maintain the population diversity.
Simultaneously, there should also be a control on the
population size in order to improve the performance of the
Algorithm. Therefore one constructs tours with a greedy
heuristic and improve this by a tour improvement heuristic
[9].
1. Fitness Function: There are two different way of
calculating fitness value of a particular chromosome.
GAs are used in maximation problem but as TSP is a
minimization problem i.e. minimium cost path is chosen
reciprocal of the maxima function can be used for deciding
the fitness value of an organism. Thus if „fi‟ denotes the
fitness values and „Di‟ represents the maximum total
Distance then fitness function can be stated as
fi = 1/ Di …(i)
A fitness function evaluation is incorporated to assigns a
value to each organism, noted as fi. This fi value is a figure
of merit which is calculated by using any domain knowledge
that applies. In principle, this is the only point in the
algorithm that domain knowledge is necessary. Organisms
are chosen using the fitness value as a guide, where those
with higher fitness values are chosen more often. Selecting
organisms based on fitness value is a major factor in the
strength of GAs as search algorithms. The method employed
here was to calculate the total Euclidean distance Di for
each organism first, then compute fi by using the following
equation
fi = D max− Di ...(ii)
where Dmax is the longest Euclidean distance over
organisms in the population [3].
3.2 Selection and Survival of the fittest
The selection operator chooses two members of the present
generation to participate in the next operations of crossover
and mutation.[3] Two different approaches for selection are
1. Roulette Selection: Assign a probability to each organism
i, computed as the proportion using the following equation
Pi=fi †Σfj ....{iii}
where j=1, 2, 3... n! [3].
2. Deterministic Sampling: Assign to each organism „i‟, a
value Si evaluated by the relation.
Si = ROUND (Pi ∗ POPSIZE ) +1 ....(iv)
(Where: ROUND means rounding off to integer and
POPSIZE means the population size).The selection operator
then assures that each organism participates as parent
exactly Si times [3].
Table -1: Fitness values and Selection Criteria
Individual
Chromosome
Di fi (as
per
eq.
(i)
fi (as
per eq.
(ii)
Pi Si
1 BCDEFA 255 0.003 120 0.133 2
2 DFEABC 175 0.005 200 0.222 2
3 CBDAEF 95 0.010 280 0.311 3
4 FEDCBA 375 0.002 0 0.001 1
5 ABCDEF 75 0.013 300 0.333 3
Σfi=900
It can easily be inferred that no matter which rule is used 5th
and 3rd individuals have higher fitness value as compared to
others and hence these two organisms will become parent
and further process continues as follows.
3.3 Crossover
Crossover is a process of generating an offspring from
selected two parents. The genes of the both the parents are
converged and combined in order to obtain two children.
Following are the different types of crossover operators.
3.3.1 Single Point Crossover
Only one cross point is used. Children are generated by
swapping the alleles after second cross point of both the
parents.
Example :-
3.3.2 Two Point Crossover
Two cross over points are used. Swapping any the middle
section will result in the production of new child. From the
examples it can be clearly seen that both Single point and
two point crossovers may produce an invalid child. Some
cities are visited more than once while some are not even
visited at least once.
Parent 1: ABCG|HBDE
Parent 2: ADEF|GCBH
Child 1: ABCG|GCBH
Child 1: ADEF|HBDE

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Thus, some modifications are desirable in these crossovers
in order to have valid organisms after reproduction.
Example:-
3.3.3 Order Crossover
To apply order crossover, two random cross points are
selected. Alleles from parent1 that fall between the two
cross points are copied into the same positions of the
offspring. The remaining allele order is determined by
parent2. Non duplicative alleles are copied from parent2 to
the offspring beginning at the position following the second
cross point. Both the parent2 and the offspring are traversed
circularly from that point.
Fig -1: Genetic Algorithm Flowchart
A copy of the parent′s next non duplicative allele is placed
in the next available child position. An Example of OX is
given below with two random cross points; 3 and 6, the
alleles in the crossing sites from parent1 (GHB) are copied
into the same positions of the child. The alleles after second
cross point in parent2 (BA), B is skipped since it already
exists in child; therefore only A is copied to child at position
7. Traverse parent2 circularly, the alleles (HDE), skip H to
D which is copied to child at position 8. Traversed Child
circularly, the alleles from parent2 (EF) are copied to child
at positions 1 and 2. Finally, skip G to C and copy it to child
at position 3 [9].
Example :-
3.3.4 Partially Matched Crossover
PMX proceeds just as OX. Alleles from parent1 that fall
between two randomly selected crossing sites are copied
into the same positions of the offspring. The remaining
allele‟s positions are determined by parent2 during a two
step process. First, alleles in parent2 not within crossing
sites are copied to the corresponding positions within the
offspring. Next each allele of parent2 within the crossing
sites is placed in the offspring at the position occupied in
parent2 by the allele from parent1 that displaced it. See the
example below. The random crossing points are 3 and 6, the
alleles in the crossing site of parent1 (AHG) displace in the
child (DEF), and then the alleles (BC) in parent2 which are
not in crossing site are copied into the same positions of the
child. D goes to position 1 which is the position with respect
to parent2, displacing allele A. Finally E and F are placed in
the positions of H and G, respectively [9].
Example :-
3.4 Mutation
In order to maintain the uniqueness and diversity in the
generated population. Mutation just alters the result by a
small amount. It randomly selects a location and performs
some modification. This modification can be swapping of a
single bit or inverting a bit value.
3.4.1 Need for Mutation
Organisms with less fitness values are discarded after every
successful iteration. This elimination process might result in
some loss of genetic information. Random occasional
modification by means of mutation ensures maintenance of
that lost genetic information which selection and crossover
cannot guarantee. Thus, Mutation helps in preventing the
loss of genetic information, maintains uniqueness and
ensures diversity in the newly generated population.
4. PARALLEL GA BASED ON HADOOP
MAPREDUCE
In this section we present the design of the proposed parallel
Genetics Algorithm using Hadoop Map Reduce.
4.1 Parallelizing Genetics Algorithm
Genetics Algorithm as stated earlier is an effective
mechanism to find acceptable solution to problems in
business, engineering and science. GA‟s are generally able
to find satisfactory solutions in reasonable amount of time,
but as they are applied to more complicated and bigger
problems there is an increase in the time required to find an
Parent 1: ABC|GHB|DE
Parent 2: ADE|FGC|BH
Child 1: ABC|FGC|DE
Child 1: ADE|GHB|BH
Parent 1: ABC|GHB|DE
Parent 2: HDE|FGC|BA
Child 1: EFC|GHB|AD
Child 1: ABH|FGC|DE
Parent 1: FBC|AHG|DE
Parent 2: ABC|DEF|GH
Child 1: DBC|AHG|FE

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acceptable solution. However their exist some problems in
their utilisation part which are as follows :-
1. For some kind of problems, the population needs to be
very large and the memory required to store each individual
may be considerable (for e.g:- genetics programming). In
such cases running and application using a single GA is
inefficient, so parallel form of GA is necessary.
2. Sequential GAs may get trapped in a sub-optimal region
of the search space thus becoming unable to find better
quality solutions. PGAs can search in parallel different
subspaces of the search space , thus making it less likely to
be trapped by low quality sub-spaces [11].
As a consequence, multiple efforts are made to make GA
faster, and one of the most promising choice is to use
parallel implementation of GA. So the parallelization of the
Genetics Algorithm is the most significant aspect of the
project and the parallelization of the genetic algorithm
would ultimately reduce the run time of the algorithm. GA
falls into three different classes which includes Global,
Coarse-Grained, Fine-Grained and Hybrid. Global PGAs
use a single population and simply parallelize the evaluation
of the fitness, and then sequentially produce the next
generation. Coarse-grained parallelization involves evolving
many separate subpopulations, called demes, in parallel.
Coarse-grained PGAs often also implement migration,
allowing an individual to move from one deme to another.
Fine-grained parallelization involves assigning one
individual per processor core. This is usually undertaken
with special hardware. Hybrid PGA is the combination of all
the above techniques [12]. Also, the advantages of using
various PGAs facilitates no changes in the traditional GA.
Parallel Genetic Algorithms divide the search space into
many smaller pieces to find the near optimal solution.
During this process the sub-optimal solutions need to avoid
local minima. Different frameworks are available for
parallelization such as Hadoop, Open-cl, Parallel Java, and
C-MPI. Among all these frameworks this project would be
implemented using Hadoop which is the most popular and
flexible one.
4.2 Hadoop
Hadoop is an Apache project being built and used by a
global community of contributors, using the Java*
programming language. It doesn‟t maintain indexes or
relationships; you don‟t need to decide how you want to
analyze your data in advance. It breaks data into manageable
chunks, replicates them, and distributes multiple copies
across all the nodes in a cluster so you can process your data
quickly and reliably later. Hadoop is also use to conduct
analysis of data [12]. It is licensed under the Apache 2.0.
Hadoop is very scalable and has a robust file system which
is designed to handle all the intra-communication between
different nodes in a cluster. It is fault tolerant and recovers
from failure if one of the data nodes suffers from failure.
Hadoop framework consists of two main layers Hadoop
Distributed File System (HDFS) and Execution engine
(MapReduce). Hadoop introduce many components like
MapReduce, Hive, H-base, HDFS, Pig, Chukwa, Avro,
Hive, ZooKeeper etc [12].Yahoo!, has been the largest
contributor to this project, and uses Apache Hadoop
extensively across its businesses. Other contributors and
users include Facebook, LinkedIn, eHarmony, and eBay.
4.2.1 MapReduce Framework
The MapReduce framework was developed by Google. It
enables highly fault-tolerant massively scalable computation
to occur on a network of commodity hardware. MapReduce
has proven successful at allowing computation over
terabytes of data to become routine [12]. MapReduce' is a
framework for processing parallelizable problems across
huge datasets using a large number of computers (nodes),
collectively referred to as a cluster or grid. Computational
processing can occur on data stored either in a files system
(unstructured) or in a database (structured). MapReduce is
an elegant and flexible paradigm which enables to develop
large-scale distributed applications [13].
4.2.2 Outline of MapReduce
The MapReduce model works by splitting large tasks into
smaller tasks. The smaller tasks are executed in parallel. The
Map Reduce framework handles the Intra cluster
communication. Map Reduce has two important steps, the
map and reduce function. The data flows from the mapper to
the Reducers. The parallelism occurs in the mapper phrase.
The data is pre-processed; it‟s converted to key value pairs.
The key value pairs are passed to the map function. The
results from different mappers are combined or merged in
the reducer phase. A reducer takes in a key value pair and
produces a collection of new values [14]. All the
fundamental steps of Genetics Algorithm are carried out in
parallel and the above process is repeated till the optimal
value is reached.
4.3.3 Logical View of MapReduce
The Map and Reduce functions of MapReduce are both
defined with respect to data structured in (key, value) pairs.
Map takes one pair of data with a type in one data domain,
and returns a list of pairs in a different domain: Map(k1,v1)
→ list(k2,v2). The Map function is applied in parallel to
every pair in the input dataset. This produces a list of pairs
for each call. After that, the MapReduce framework collects
all pairs with the same key from all lists and groups them
together, creating one group for Logical view of Map
Reduce: The Map and Reduce functions of MapReduce are
both defined with respect to data structured in (key, value)
pairs. Map takes one pair of data with a type in one data
domain, and returns a list of pairs in a different domain:
Map(k1,v1) → list(k2,v2). The Map function is applied in
parallel to every pair in the input dataset. This produces a
list of pairs for each call. After that, the MapReduce
framework collects all pairs with the same key from all lists
and groups them together, creating one group for each key
[15].

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Fig -2: Hadoop MapReduce Model
The Reduce function is then applied in parallel to each
group, which in turn produces a collection of values in the
same domain: Reduce(k2, list (v2)) → list(v3). Each Reduce
call typically produces either one value v3 or an empty
return, though one call is allowed to return more than one
value. The returns of all calls are collected as the desired
result list. Thus the MapReduce framework transforms a list
of (key, value) pairs into a list of values [15].
CONCLUSION
In this paper we have discussed the Travel Planning System
using Genetics Algorithm to assist the user to obtain
optimal tour to visit n cities. It has also proposed the use of
advanced operators of Genetic Algorithms in order to
enhance the rate of divergence, and achieved tours with
reasonable time. The obtained results highlighted that using
Parallel Genetics Algorithm allowed us to save more time.
and the Map Reduce model helps to increase the processing
speed. Certain aspects in the project such as the parallel
design of GA can be enhanced in the future. An enhanced
parallel design can incorporate more computation of
selection, crossover or mutation in the mapper phase. This
will put less pressure on the reducers. The architecture can
be improved further by adding more reducers
REFERENCES
[1]. Konstantinos G.Zografos and Michael A.Madas, “ A
Travel and Tourism System Providing Readl-Time,
Value Added Logistical Services on the Move”, Athens
University of Economics & Business(AUEB).
[2]. Dino Keco and Abdulhamit Subasi, “ Parallelization of
genetics algorithm using Hadoop Map/Reduce”.
[3]. Buthainah Fahran Al-Dulaimi, and Hamza A. Ali,
“Enhanced Travelling Salesman Problem Solving by
Genetic Algorithm Technique (TSPGA) in paper 2008.
[4]. Kylie Bryant and Arthur Benjamin, ”Genetics
Algorithm and the Travelling Salesman Problem”,
thesis, Department of Mathematics, Harvey Mudd
College, Dec. 2000.
[5]. Introduction to Genetics Algorithm (1996). [Online].
Available:
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/h
mw/article1.html.
[6]. Noraini Mohd Razali and John Geraghty, “Genetic
Algorithm Performance with Different Selection
Strategies in Solving TSP flwcchart,” in Proceedings of
the World Congress on Engineering 2011 Vol II WCE
2011,paper 06.08.11, London, U.K.
[7]. Ashish Gupta and Shipra Khurana, “ Study of
Travelling Salesman Problem using Genetics
Algorithm”,vol 2,issue 5,pp 575-588, May 2012.
[8]. Genetics Algorithm (1996) Web page on
www.doc.ic.ac.uk. [Online]. Available:
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/tc
w2/report.html.
[9]. Konstantinos G.Zografos and Michael A.Madas, “ A
Travel and Tourism System Providing Readl-Time,
Value Added Logistical Services on the Move”, Athens
University of Economics & Business(AUEB)..
[10].Varshika dwivedi, Taruna Chauhan, Sanu Saxena and
Princie Agrawal, “Travelling Salesman Problem using
Genetics Algorithm”.
[11].Mariusz Nowastawski and Riccardo Poli, “ Parallel
Genetic Algorithm Taxonomy”, Proc‟ 99,paper May
13,1999.
[12].Rahate Kanchan Sharadchandra and L.M.R.J Lobo, “
Parallelization of Genetics Algorithm using
Hadoop”,vol 1,paper Nov-12.
[13].Linda Di Geronimo, Filomena Ferrucci, Alfonso
Murolo and Federica Sarro, “ A Parallel Genetics
Algorithm Based on Hadoop MapReduce for the
Automatic Generation of JUnit Test Suites”.
[14].Akshat Mishra ,‟Genetic Algorithm for the Travelling
Salesman Problem on Hadoop”,Tech. Rep. 2011.
[15].Map Reduce (2014) Webpage on Wikipedia [Online].
Available: http://en.wikipedia.org/wiki/MapReduce.
[16].Introduction to Genetics Algorithm (2014) Webpage on
obitko [Online]. Available:
http://www.obitko.com/tutorials/genetic-algorithms/ga-
basic-description.php
BIOGRAPHIES
Md. Lutful Islam is an Assistant Professor
at M.H Saboo Siddik College of Engg. His
qualification includes M.Tech from Aligarh
Muslim University and M.C.A from
Rajasthan Vidyapeeth, Udaipur. He has a
teaching experience of 15 yrs. His key areas
of interests are Computer Graphics, Discrete Mathematics,
Robotics and Artificial Intelligence.
Danish Pandhare is a final year student(B.E)
of M.H.Saboo Siddik college of Engineering,
Byculla, Mumbai University. He holds a
Diploma in Computer Engineering from
Vidya Prasarak Mandals Polytechnic,
MSBTE. His key areas of interests include
Web Designing, Cloud Computing , Networkng, Analysis of
Algorithm and Design

_______________________________________________________________________________________
Arshad Makhthedar is a final year
student(B.E) of M.H.Saboo Siddik college
of Engineering, Byculla, Mumbai
University. He holds a Diploma in Computer
Engineering from M.H Saboo Siddik
Polytechnic, MSBTE. His key areas of
interests include AI and Soft Computing, Mathematics and
Statistics.
Nadeem Shaikh is a final year student(B.E)
of M.H.Saboo Siddik college of
Engineering, Byculla, Mumbai University.
His key areas of interests include
Distributed and Internet Computing
Systems, Web Security, Web based
Systems, and Web Security.

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