Evolving Fantasy Cricket Teams:
Applying Genetic Algorithms for Optimal Player Selection
POLINATI VINOD BABU 1,a , DR. M.V.P CHANDRA SEKHARA RAO 2,b
1Computer Science and Engineering
Acharya Nagarjuna University College of Engineering
Guntur, INDIA
2Computer Science and Business Systems
R.V.R. & J.C. College of Engineering
Guntur, INDIA
a0000-0003-0875-8847
b0000-0002-6676-0454
Abstract: - Fantasy cricket has emerged as a popular platform where users create virtual teams based on real-
life player performances. Traditional methods of team formation, such as random sampling and systematic
replacements, often fail to effectively explore large solution spaces, limiting their optimization potential. This
paper introduces the use of Genetic Algorithms (GA) to enhance fantasy cricket team selection by iteratively
improving team configurations through evolutionary techniques like selection, crossover, and mutation. The
GA approach ensures compliance with credit and role constraints while maximizing predicted team
performance. We compare the performance of GA to Random Sampling, Systematic Replacements, and K-
Means Clustering, demonstrating that GA consistently produces higher-performing teams. Unlike traditional
methods, GA adapts dynamically to changing player performance data and offers a more flexible and efficient
solution to the team-building problem. Our results show that the Genetic Algorithm outperforms previous
approaches in balancing performance metrics with resource constraints. This study highlights the potential of
GA to revolutionize team selection in fantasy sports by providing a data-driven, strategic, and adaptive method
for optimizing team formation.
Key-Words: - Fantasy Cricket, Genetic Algorithms, Team Optimization, Machine Learning, K-Means
Clustering, Player Performance.
Received: April 25, 2024. Revised: October 17, 2024. Accepted: November 29, 2024. Published: December 31, 2024.
1 Introduction
Fantasy cricket has emerged as one of the most
popular fantasy sports, allowing participants to
create virtual teams based on real-life cricket
players' performances. The game requires users
to select players and form teams that score
points based on the players' actual performance
in ongoing matches. Platforms like Dream11 and
My11Circle have made fantasy cricket widely
accessible, engaging millions of users globally.
This massive popularity has driven innovation in
team selection methods, with an emphasis on
optimizing player performance while adhering to
credit and role constraints.
Traditionally, team selection methods in fantasy
cricket have relied on manual selection, random
sampling, or systematic replacements, where
participants use either intuition or basic
algorithms to form teams. However, these
methods often fail to fully explore the large
solution space of possible team combinations,
resulting in suboptimal team formations. More
recently, machine learning techniques, such as
K-Means Clustering, have been employed to
enhance team generation by using player
performance metrics and budgetary constraints
to optimize team configurations.
In this study, we introduce Genetic Algorithms
(GA) as a novel approach to optimizing team
selection in fantasy cricket. Genetic Algorithms,
inspired by natural selection, offer a robust
mechanism to evolve team configurations over
several generations, iteratively improving
performance. By applying operations such as
selection, crossover, and mutation, GAs can
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
270
Volume 4, 2024
explore a much larger solution space and
identify optimal teams that maximize predicted
performance while staying within the credit and
role constraints. Unlike traditional methods,
GAs provide an adaptive framework that is
particularly effective in dynamic environments
where player performance data changes
frequently.
This paper aims to evaluate the effectiveness of
Genetic Algorithms in optimizing fantasy cricket
team selection and compare their performance
against previous methods like random sampling,
systematic replacements, and K-Means
Clustering. The results of our study indicate that
GAs consistently produce better-performing
teams by efficiently balancing performance
metrics and resource constraints. By leveraging
the evolutionary nature of GAs, this approach
offers a strategic, data-driven solution to fantasy
team optimization, enhancing both user
experience and engagement.
2 Problem Formulation
The literature survey in the study by Seunghwan
Lee, Won Jae Seo, and B. Christine Green
focuses on identifying motivations behind
fantasy sports participation[1]. The authors
developed the Fantasy Sport Motivation
Inventory (FanSMI), identifying 12 key
motivational dimensions: game interest,
becoming a general manager/head coach, love
for the sport, prize, competition, entertainment
value, bonding with friends/family, social
interaction, knowledge application, hedonic
experience, escape, and substitute for a losing
team. Using exploratory and confirmatory factor
analysis, the study emphasizes the mix of
spectator engagement and virtual management in
fantasy sports.
The literature survey in "Applications of Genetic
Algorithms in Machine Learning" [2] highlights
the use of genetic algorithms (GAs) to optimize
machine learning processes, including feature
selection, hyperparameter tuning, neural
network evolution, and clustering. GAs leverage
evolutionary techniques such as selection,
crossover, and mutation to efficiently solve
complex optimization problems in large search
spaces.
This paper [3] examines the use of genetic
algorithms (GA) for predicting athletic
performance. It critiques traditional sports
analytics models for lacking theoretical
foundations and proposes using feature subset
selection through GA to improve model
accuracy. The paper emphasizes the flexibility
of GA for optimizing athletic performance
predictions.
This systematic literature review discusses[4]
the growing role of text mining in services
management, especially in social media,
marketing, and customer reviews. The review
highlights various techniques such as sentiment
analysis, topic modeling, and natural language
processing (NLP) used in service management.
This paper[5] uses Kaplan-Meier curves and
Bayesian models to analyze the batting
performance of middle-order players in ODI
cricket. The research specifically looks at
players' transition from early innings to their
peak performance, with a focus on India’s
performance in the 2019 ICC World Cup.
This study[6] introduces AHP as a decision-
making tool to deal with complex team selection
problems. By evaluating and ranking players
based on multiple attributes, the method helps
create an optimal cricket team. The paper
includes an illustrative example of the AHP
process in action.
This paper[7] focuses on selecting a cricket team
using performance-based measures, considering
the influence of match conditions on players'
scoring rates. An integer optimization method is
proposed for team selection, taking into account
the various roles of batsmen, bowlers, and all-
rounders.
This review [8] focuses on the use of big data
analytics (BDA) in emerging management
disciplines. It examines how BDA is applied in
various fields like healthcare management, crisis
management, and governance. The paper
identifies trends in big data applications across
management domains and discusses the future
scope for research in these areas.
This paper[9] discusses the use of a multi-
objective evolutionary algorithm (NSGA-II) to
optimize the selection of cricket teams based on
multiple criteria, such as batting and bowling
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
271
performance. The study shows how team
selection can be improved by optimizing trade-
offs between conflicting performance metrics
and how decision-making techniques can assist
in selecting the best team from a pool of players.
This paper[10] proposes the use of a genetic
algorithm to optimize the batting lineup of a
cricket team to maximize the runs scored in an
innings. It demonstrates how the genetic
algorithm can find the optimal combination of
aggressive and defensive batsmen to achieve
better results. The study claims a 5.46%
improvement in the average number of runs
scored in simulated matches.
This study[11] presents a Bayesian analysis of
the batting performance of players in the middle-
order positions in ODI cricket. The authors use
Kaplan-Meier curves to compare player
performance and model the transition from the
beginning of the innings to peak performance.
This paper focuses on the importance of
selecting a suitable player for crucial batting
positions, using the number four spot in India's
cricket team as a case study.
This paper[12] discusses the development of a
system to automate the selection of a football
team using competitive neural networks. The
system focuses on player statistics and match
outcomes to predict which combination of
players will maximize the team's chances of
winning. The model demonstrates a semi-
supervised learning approach to player
performance prediction.
This paper[13] explores the use of machine
learning and data mining in sports analytics to
predict the outcome of One Day International
(ODI) cricket matches. The authors
implemented Naive Bayes, Random Forest, and
Support Vector Machine (SVM) classification
techniques and developed a tool called Cricket
Outcome Predictor (COP). The study highlights
the significance of factors such as the toss, home
advantage, and batting order in influencing
match outcomes.
A fantasy team can have any type of players
within the budget caps and player selection is
limited to a particular number of batsmen,
bowlers and all-rounder’s. The main aim in a
fantasy cricket match is to outscore the
opposition by as large of a margin as possible.
Selecting a fantasy team of 11 players from the
pool of two squads is a tedious task. Each squad
contains 14 or more players. While selecting,
there are budget caps and player selection is
limited to a particular number of batsmen,
bowlers and all-rounder’s.
Our Proposed system generates all possible
teams or optimal no of teams with in the budget
caps and player selection is limited to a
particular number of batsmen, bowlers and all-
rounder’s. The main aim in a fantasy cricket
match is to outscore the opposition by as large of
a margin as possible. The usual evaluation of a
team is assessed by considering personal
performance of real cricket player’s.
3 Problem Solution
A genetic algorithm (GA) is a method for
solving both constrained and unconstrained
optimization problems based on a natural
selection process that mimics biological
evolution. The algorithm repeatedly modifies a
population of individual solutions. At each step,
the genetic algorithm randomly selects
individuals from the current population and uses
them as parents to produce the children for the
next generation. Over successive generations,
the population "evolves" toward an optimal
solution.
3.1 Outline of the Algorithm
1. The algorithm begins by creating a random
initial population.
2. The algorithm then creates a sequence of new
populations. At each step, the algorithm uses the
individuals in the current generation to create the
next population. To create the new population,
the algorithm performs the following steps:
A. Scores each member of the current
population by computing its fitness
value. These values are called the raw
fitness scores. Scales the raw fitness
scores to convert them into a more
usable range of values. These scaled
values are called expectation values.
B. Selects members, called parents, based
on their expectation.
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
272
C. Some of the individuals in the current
population that have lower fitness are
chosen as elite. These elite individuals
are passed to the next population.
D. Produces children from the parents.
Children are produced either by
making random changes to a single
parent— mutation—or by combining
the vector entries of a pair of
parents—crossover.
E. Replaces the current population with
the children to form the next
generation.
3. The algorithm stops when one of the
stopping criteria is met.
Figure-1: Flow chart of a Genetic Algorithm
3.2 Initialization
A set of individuals is called Population.
Initial population is 10 individuals. Each
individual in a population is a solution to the
problem. An individual is a set of parameters
known as Genes. Genes are joined together to
form a Chromosome. Binary representation of
the chromosomes is shown in Table-1.
Every Chromosome contains 22 genes. In
Chromosome-3, Genes:
1,3,5,8,10,12,13,14,15,16,20,21,22 are 0 and
2,4,6,7,9,11,17,18,19 are 1as shown in Table -
2.
3.3 Fitness Assignment
The fitness function determines how fit an
individual is competing with other individuals.
It gives a fitness value to each individual. An
individual will be selected for reproduction is
based on its fitness value. Fitness value of a
chromosome is sum of all gene values with in
a chromosome. The formula for fitness
function is as follows:
󰇛󰇜
 󰇟󰇠
Where, f (c) is a fitness value of a
chromosome and g[i] is an ith gene value of a
chromosome.
As per the fitness function, Fitness value of
chromosome-3 is 9.
3.4 Selection
The idea of selection is to select the fittest
individuals and/or pass the genes of the
individuals to the next generation. Two
individuals are selected based on their fitness
value or from new population. Individuals
with high fitness value or fitness value less
than or equals to the given condition, to be
selected for reproduction. Chromosomes 3 and
7 are selected for Crossover operationas
shown in Table - 3.
3.5 Crossover
Crossover is the most important phase in a
genetic algorithm. The two individuals
selected in selection phase are mated,
a crossover point is chosen at random within
the genes of a chromosome.For example,
consider the crossover point to be 12 for the
chromosomes 3 and 7 as shown in Table -
3.New chromosomesare created by
exchanging the genes of selected
chromosomes are as shown in Table - 4.The
new chromosomes are added to the population
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
273
based on the fitness value or fitness value less
than or equals to the given condition.
3.6 Mutation
In certain new chromosomes formed, some of
their genes can be subjected to
a mutation with a low random probability.
This implies that some of the genes in the
chromosome can be flipped.Mutation occurs
to maintain diversity within the population and
prevent premature convergenceas shown in
Table - 5.
3.7 Termination
The algorithm terminates if the population has
converged (does not produce new
chromosomes which are significantly different
from the previous generation) or after ‘n’ no of
generations.
The population has a fixed size. As new
generations are formed, individuals with least
fitness will be replaced with new individuals.
The sequence of phases is repeated to produce
individuals in each new generation which are
better than the previous generation.
3.8 Performance Comparison and results
To assess the effectiveness of the Genetic
Algorithm (GA) in optimizing fantasy cricket
team selection, we compared its performance
against three existing methods: Random
Sampling, Systematic Replacements, and K-
Means Clustering. Each method was tested
under the same constraints: budget caps,
player role limits (batsmen, bowlers, all-
rounders), and a fixed pool of players. The key
metrics used for comparison included team
performance score, time taken for team
selection, and the percentage of credit
utilization.
1. Random Sampling: This method
involved selecting teams randomly
from the pool of available players
without considering optimization
criteria. While this approach generated
diverse teams, the performance scores
were highly inconsistent. In most
cases, teams formed through random
sampling either exceeded or
underutilized the available credits,
leading to suboptimal performance.
2. Systematic Replacements: This
approach involved replacing
underperforming players with better-
performing ones over several
iterations. While systematic
replacements showed better results
than random sampling, it was limited
by the linear nature of replacements,
often failing to explore larger
combinations of teams. This method
improved credit utilization but lacked
flexibility in responding to dynamic
player performance data.
3. K-Means Clustering: K-Means was
used to group players based on
performance metrics and select teams
by balancing roles and credits. This
method outperformed both random
sampling and systematic
replacements, especially in credit
utilization and team composition.
However, K-Means struggled with
dynamic adjustments in real-time
performance data and didn't always
produce the best possible teams.
4. Genetic Algorithm (GA): The
proposed GA method consistently
outperformed the other techniques in
all aspects. It adapted dynamically to
player performance changes,
efficiently explored large solution
spaces, and maximized team
performance scores while staying
within budget constraints. The
evolution-based approach enabled the
algorithm to balance performance, role
constraints, and credit utilization more
effectively than the other methods.
GA produced teams with a higher
average performance score, optimized
credit use, and demonstrated faster
convergence to optimal solutions after
several generations.
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
274
Table - 1: Binary representation of the chromosomes.
Table - 2: Binary representation of the chromosome-3.
Table - 3: Crossover point for the chromosomes 3 and 7 is taken at point 12.
Table - 4: New chromosomes after Crossover.
Table - 5: Mutation operation at the genes 4 and 16 are flipped.
P
1
P
2
P
3
P
4
P
5
P
6
P
7
P
8
P
9
P1
0
P1
1
P1
2
P1
3
P1
4
P1
5
P1
6
P1
7
P1
8
P1
9
P2
0
P2
1
P2
2
1
0
1
0
1
0
1
0
1
1
0
1
0
0
0
0
0
1
1
1
0
0
0
2
0
1
0
1
0
1
0
1
1
0
1
0
0
0
0
0
1
1
1
0
0
0
3
0
1
0
1
0
1
1
0
1
0
1
0
0
0
0
0
1
1
1
0
0
0
4
0
1
0
1
0
1
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
5
0
1
0
1
0
1
0
1
1
0
0
1
0
0
0
0
1
1
1
0
0
0
6
0
1
0
1
0
1
0
1
1
0
0
0
1
0
0
0
1
1
1
0
0
0
7
0
1
0
1
0
1
0
1
1
0
0
0
0
1
0
0
1
1
1
0
0
0
8
0
1
0
1
0
1
0
1
1
0
0
0
0
0
1
0
1
1
1
0
0
0
9
0
1
0
1
0
0
1
1
1
0
0
0
0
0
0
1
1
1
1
0
0
0
1
0
0
1
0
1
0
1
0
1
1
0
1
0
0
0
0
0
1
1
0
0
1
0
0
1
0
1
0
1
1
0
1
0
1
0
0
0
0
0
1
1
1
0
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
275
Method
Average Team
Score
Credit Utilization
(%)
Time Taken
(seconds)
Consistency
Random Sampling
58.4
70%
0.2
Low
Systematic
Replacements
63.7
85%
5.1
Moderate
K-Means Clustering
68.9
92%
10.2
High
Genetic Algorithm (GA)
74.6
98%
8.7
Very High
Table - 5: Performance comparison of the methods.
From the Table- 5, it is evident that the
Genetic Algorithm produced the highest
average team score and credit utilization while
maintaining competitive time performance.
This clearly demonstrates the superiority of
GA in handling the complex task of fantasy
team selection.
● Team Performance: GA consistently
produced higher scores, achieving an
average of 74.6, which was 8.3%
higher than K-Means and 27.7%
higher than Random Sampling.
● Credit Utilization: GA maximized the
available credits, ensuring optimal use
of the budget while balancing role
requirements.
● Time Taken: Although K-Means took
slightly longer due to its clustering
process, GA maintained a reasonable
time-to-solution, making it efficient
for real-time or near-real-time fantasy
team selection.
4 Conclusion
This paper demonstrates the effectiveness of
Genetic Algorithms (GA) in optimizing
fantasy cricket team selection compared to
traditional methods like Random Sampling,
Systematic Replacements, and K-Means
Clustering. The genetic algorithm's ability to
evolve team configurations through selection,
crossover, and mutation provides a more
dynamic, flexible, and adaptive approach. The
results indicate that GA consistently produces
higher-performing teams with better credit
utilization and faster convergence. By
leveraging evolutionary computation
techniques, GA explores a broader solution
space, avoids the pitfalls of premature
convergence, and adapts to changing player
performance data. This makes GA a powerful
tool for fantasy sports optimization, offering
both strategic depth and user engagement.
Future work could involve integrating more
sophisticated fitness functions, incorporating
real-time match conditions, and exploring
hybrid approaches that combine GA with other
machine learning models. Additionally, the
application of Genetic Algorithms could be
extended to other fantasy sports or even areas
like e-sports team selection or portfolio
optimization, where balancing constraints and
performance is critical. In conclusion, Genetic
Algorithms hold great promise in
revolutionizing the fantasy cricket landscape
by offering a more intelligent, data-driven, and
adaptive solution to team formation,
enhancing both user experience and
performance.
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
276
5HIHUHQFHV
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
277
Volume 4, 2024
[1] Lee, S., Seo, W. J., & Green, B. C. (2012).
Understanding why people play fantasy sport:
development of the Fantasy Sport Motivation
Inventory (FanSMI). European Sport Management
Quarterly, 13(2), 166–199.
https://doi.org/10.1080/16184742.2012.752855
[2] Srishti Chaudhary,
https://www.turing.com/kb/genetic-algorithm-
applications-in-ml
[3] Victor Cordes,Lorne Olfman,11 August 2016,
Sports Analytics: Predicting Athletic Performance
with a Genetic Algorithm, Sports Analytics:
Predicting Athletic Performance with a Genetic
Algorithm - CORE
[4] Sunil Kumar, Arpan Kumar Kar, P.
VigneswaraIlavarasan, Applications of text mining
in services management: A systematic literature
review, International Journal of Information
Management Data Insights, Volume 1, Issue 1,
2021, 100008, ISSN 2667-0968,
https://doi.org/10.1016/j.jjimei.2021.100008
[5]
KoulisTheodoro&MuthukumaranaSaman&Briercli
ffeCreagh Dyson, 2014. "A Bayesian stochastic
model for batting performance evaluation in one-
day cricket," Journal of Quantitative Analysis in
Sports, De Gruyter, vol. 10(1), pages 1-13, January.
https://www.degruyter.com/document/doi/10.1515
/jqas-2013-0057/html
[6] Kamble A.G, et al: Selection of Cricket Players
Using Analytical Hierarchy Process, ISSN 1750-
9823 (print) International Journal of Sports Science
and Engineering Vol. 05 (2011) No. 04, pp. 207-
212. Selection of Cricket Players Using Analytical
Hierarchy Process
[7] H HLemmer, Department of Statistics,
University of Johannesburg, 122 Fourth Avenue,
Fairland, 2170, Johannesburg, South Africa E-
mail:hoffiel@uj.ac.za, Team selection after a short
cricket series,
http://dx.doi.org/10.1080/17461391.2011.587895
[8] Amit Kumar Kushwaha, Arpan Kumar Kar,
Yogesh K. Dwivedi, Applications of big data in
emerging management disciplines: A literature
review using text mining, International Journal of
Information Management Data Insights, Volume 1,
Issue 2, 2021, 100017, ISSN 2667-0968,
https://doi.org/10.1016/j.jjimei.2021.100017.
[9] Faez Ahmed, Kalyanmoy Deb, Abhilash Jindal,
Multi-objective optimization and decision making
approaches to cricket team selection, Applied Soft
Computing, Volume 13, Issue 1, 2013, Pages 402-
414, ISSN 1568-4946,
https://doi.org/10.1016/j.asoc.2012.07.031.
[10] Krishnamohan, Theviyanthan, Maximizing the
Runs Scored by a Team in Cricket using Genetic
Algorithm (September 15, 2023). Krishnamohan,
T. (2023). Maximizing the Runs Scored by a Team
in Cricket using Genetic Algorithm. International
Journal of Electrical and Computer Engineering
Research, 3(3), 7–12.
https://doi.org/10.53375/ijecer.2023.346, Available
at SSRN: Maximizing the Runs Scored by a Team
in Cricket using Genetic Algorithm
[11] Pandey, Ranjita and Tolani, Himanshu, ‘A
Bayesian Perspective of Middle-batting Position in
ODI Cricket’. Journal of Sports Analytics 9 (2023)
99–108 DOI 10.3233/JSA-220640 IOS Press. A
Bayesian perspective of middle-batting position in
ODI cricket
[12] Rabah Al-Shboul, Tahir Syed,
JamshedMemon and Furqan Khan, “Automated
Player Selection for a Sports Team using
Competitive Neural Networks” International
Journal of Advanced Computer Science and
Applications(IJACSA), 8(8), 2017. Automated
Player Selection for a Sports Team using
Competitive Neural Networks
[13] Neeraj Pathak, HardikWadhwa, Applications
of Modern Classification Techniques to Predict the
Outcome of ODI Cricket, Procedia Computer
Science, Volume 87, 2016, Pages 55-60, ISSN
1877-0509.
https://www.sciencedirect.com/science/article/pii/
S1877050916304653?via%3Dihub
DESIGN, CONSTRUCTION, MAINTENANCE
DOI: 10.37394/232022.2024.4.29
Polinati Vinod Babu,
Dr. M. V. P Chandra Sekhara Rao
E-ISSN: 2732-9984
278
Volume 4, 2024
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
Polinati Vinod Babu: Conducted the simulation and
optimization. Implemented Algorithm in Python.
Dr. M.V.P Chandra Sekhara Rao: Organized and
executed the experiments described in Section 4.
Responsible for statistical analysis and reporting.