The document describes genetic algorithms and their application to supervised and unsupervised learning problems. It provides details on the genetic learning process, including initializing a population, evaluating fitness, and applying genetic operators like crossover and mutation to generate new solutions. An example of applying genetic algorithms to supervised learning for classifying customers as interested or not interested in life insurance is presented. The document also discusses using genetic algorithms for unsupervised clustering, providing examples of calculating fitness scores and evolving solution populations. Key aspects of choosing genetic algorithms or other data mining techniques are outlined.

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Genetic Algorithm
Genetic Algorithms (GA) apply an evolutionary
approach to inductive learning. GA has been
successfully applied to problems that are
difficult to solve using conventional techniques
such as scheduling problems, traveling
salesperson problem, network routing problems
and financial marketing.

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Supervised genetic learning

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Genetic learning algorithm
• Step 1: Initialize a population P of n elements
as a potential solution.
• Step 2: Until a specified termination condition
is satisfied:
– 2a: Use a fitness function to evaluate each
element of the current solution. If an element
passes the fitness criteria, it remains in P.
– 2b: The population now contains m elements (m
<= n). Use genetic operators to create (n – m)
new elements. Add the new elements to the
population.

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Digitalized Genetic knowledge
representation
• A common technique for representing
genetic knowledge is to transform
elements into binary strings.
• For example, we can represent income
range as a string of two bits for assigning
“00” to 20-30k, “01” to 30-40k, and “11” to
50-60k.

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Genetic operator - Crossover
• The elements most often used for
crossover are those destined to be
eliminated from the population.
• Crossover forms new elements for the
population by combining parts of two
elements currently in the population.

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Genetic operator - Mutation
• Mutation is sparingly applied to elements
chosen for elimination.
• Mutation can be applied by randomly
flipping bits (or attribute values) within a
single element.

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Genetic operator - Selection
• Selection is to replace to-be-deleted
elements by copies of elements that pass
the fitness test with high scores.
• With selection, the overall fitness of the
population is guaranteed to increase.

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Step 1 of Supervised genetic learning
This step initializes a population P of
elements. The P referred to population
elements. The process modifies the
elements of the population until a
termination condition is satisfied, which
might be all elements of the population
meet some minimum criteria. An
alternative is a fixed number of iterations
of the learning process.

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Step 2 of supervised genetic learning
Step 2a applies a fitness function to
evaluate each element currently in the
population. With each iteration, elements
not satisfying the fitness criteria are
eliminated from the population. The final
result of a supervised genetic learning
session is a set of population elements
that best represents the training data.

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Step 2 of supervised genetic learning
Step 2b adds new elements to the
population to replace any elements
eliminated in step 2a. New elements are
formed from previously deleted elements
by applying crossover and mutation.

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An initial population for supervised
genetic learning example
Population
element
Income
Range
Life
Insurance
Promotion
Credit Card
Insurance
Sex Age
1 20-30k No Yes Male 30-39
2 30k-40k Yes No Female 50-59
3 ? No No Male 40-49
4 30k-40k Yes Yes Male 40-49

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Question mark in population
A question mark in the population means
that it is a “don’t care” condition, which
implied that the attribute is not important to
the learning process.

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Training Data for Genetic Learning
Training
Instance
Income Range Life Insurance
Promotion
Credit Card
Insurance
Sex Age
1 30-40k Yes Yes Male 30-39
2 30-40k Yes No Female 40-49
3 50-60k Yes No Female 30-39
4 20-30k No No Female 50-59
5 20-30k No No Male 20-29
6 30-40k No No Male 40-49

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Goal and condition
• Our goal is to create a model able to
differentiate individuals who have
accepted the life insurance promotion
from those who have not.
• We require that after each iteration of the
algorithm, exactly two elements from each
class (life insurance promotion=yes) & (life
insurance promotion=no) remain in the
population.

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Fitness Function
1. Let N be the number of matches of the input
attribute values of E with training instances
from its own class.
2. Let M be the number of input attribute value
matches to all training instances from the
competing classes.
3. Add 1 to M.
4. Divide N by M.
Note: the higher the fitness score, the smaller will
be the error rate for the solution.

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Fitness function for element 1 own
class of life insurance promotion = no
1. Income Range = 20-30k matches with
training instances 4 and 5.
2. No matches for Credit Card
Insurance=yes
3. Sex=Male matches with training
instances 5 and 6.
4. No matches for Age=30-39.
5. ∴ N = 4

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Fitness function for element 1 of competing
class of life insurance promotion = yes
1. No matches for Income Range=20-30k
2. Credit Card Insurance=yes matches with
training instance 1.
3. Sex=Male matches with training instance 1.
4. Age=30-39 matches with training instances 1
and 3.
5. ∴M = 4
6. ∴F(1) = 4 / 5 = 0.8
7. Similarly F(2)=0.86, F(3)=1.2, F(4)=1.0

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Crossover operation for elements 1 & 2

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A Second-Generation Population
Population
element
Income
Range
Life
Insurance
Promotion
Credit Card
Insurance
Sex Age
1 20-30k No No Female 50-59
2 30k-40k Yes Yes Male 30-39
3 ? No No Male 40-49
4 30k-40k Yes Yes Male 40-49

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Application of the model(test
phase)
• To use the model, we can compare a new
unknown instance (test data) with the elements
of the final population. A simple technique is to
give the unknown instance the same
classification as the population element to which
it is most similar.
• The algorithm then randomly chooses one of the
m elements and gives the unknown instance the
classification of the randomly selected element.

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Genetic Algorithms & unsupervised Clustering
Suppose there are P data instances within
the space where each data instance
consists of n attribute values. Suppose m
clusters are desired. The model will
generate k possible solutions. A specific
solution contains m n-dimensional points,
where each point is a best current
representative element for one of the m
clusters.

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For example, S2 represents one of the k possible solutions
and contains two elements E21 and E22.

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Crossover operation
A crossover operation is accomplished by
moving elements (n-dimensional points)
from solution Si to solution Sj. There are
several possibilities for implementing
mutation operations. One way to mutate
solution Si is to swap one or more point
coordinates of the elements within Si.

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Fitness function
An applicable fitness function for solution Sj is the
average Euclidean distance of the P instances in
the n-dimensional space from their closest
element within Sj. We take each instance I in P
and compute the Enclidean distance from I to
each of the m elements in Sj. Lower values
represent better fitness scores. Once genetic
learning terminates, the best of the k possible
solutions is selected as the final solution. Each
instance in the n-dimensional space is assigned
to the cluster associated with its closest element
in the final solution.

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Training data set for unsupervised GA
Instance X Y
1 1.0 1.5
2 1.0 4.5
3 2.0 1.5
4 2.0 3.5
5 3.0 2.5
6 5.0 6.0

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Fitness function for unsupervised GA
We apply fitness function to the Training data.
We instruct the algorithm to start with a solution
set consisting of three plausible solutions (k=3).
With m=2, P=6, and k=3, the algorithm
generates the initial set of solutions. An
element in the solution space contains a single
representative data point for each cluster. For
example, the data points for solution S1 are
(1,0, 1.0) and (5.0,5.0).

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Euclidean distance
)||...|||(|),( 22
22
2
11 pp j
x
i
x
j
x
i
x
j
x
i
xjid −++−+−=
Fitness score of d(1.0, 1.0) and d(5.0, 5.0)
= min ( Squareroot( |1.0 – 1.0|2
+ |1.0 – 1.5|2
), Squareroot( |5.0 – 1.0|2
+ |5.0 – 1.5|2
) +
min ( Squareroot( |1.0 – 1.0|2
+ |1.0 – 4.5|2
), Squareroot( |5.0 – 1.0|2
+ |5.0 – 4.5|2
) +
min ( Squareroot( |1.0 – 2.0|2
+ |1.0 – 1.5|2
), Squareroot( |5.0 – 2.0|2
+ |5.0 – 1.5|2
) +
min ( Squareroot( |1.0 – 2.0|2
+ |1.0 – 3.5|2
), Squareroot( |5.0 – 2.0|2
+ |5.0 – 3.5|2
) +
min ( Squareroot( |1.0 – 3.0|2
+ |1.0 – 2.5|2
), Squareroot( |5.0 – 3.0|2
+ |5.0 – 2.5|2
) +
min ( Squareroot( |1.0 – 5.0|2
+ |1.0 – 6.0|2
), Squareroot( |5.0 – 5.0|2
+ |5.0 – 6.0|2
)
= 0.5 + 3.5 + 1.11 + 2.69 + 2.5 + 1
= 11.3

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Solution Population for
unsupervised Clustering
S1 S2 S3
Solution elements (1.0,1.0) (3.0,2.0) (4.0,3.0)
(initial population) (5.0,5.0) (3.0,5.0) (5.0,1.0)
Fitness score 11.31 9.78 15.55
-----------------------------------------------------------------------------------------------------------------------------
Solution elements (5.0,1.0) (3.0,2.0) (4.0,3.0)
(second generation)(5.0,5.0) (3.0,5.0) (1.0,1.0)
Fitness score 17.96 9.78 11.34
-----------------------------------------------------------------------------------------------------------------------------
Solution elements (5.0,5.0) (3.0,2.0) (4.0,3.0)
(third generation) (1.0,5.0) (3.0,5.0) (1.0,1.0)
Fitness score 13.64 9.78 11.34
-----------------------------------------------------------------------------------------------------------------------------

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First Generation Solution
To compute the fitness score of 11.31 for solution
S1 the Euclidean distance between each
instance and its closest data point in S1 is
summed. To illustrate this, consider instance 1
in training data. The Euclidean distance between
(1.0,1.0) and (1.0,1.5) is computed as 0.50. The
distance between (5.0,5.0) and (1.0,1.5) is 5.32.
The smaller value of 0.50 is represented in the
overall fitness score for solution S1. S2 is the
best first-generation solution.

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Second Generation Solution
The second generation is obtained by
performing a crossover between solutions
S1 and S3 with solution element (1.0,1.0)
in S1 exchanging places with solution
element (5.0,1.0) is S3. The result of the
crossover operation improves (decreases)
the fitness score for S3 while the score for
S1 increases.

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(Final) Third Generation Solution
The third generation is acquired by mutating
S1. The mutation interchanges the y-
coordinate of the first element in S1 with
the x-coordinate of the second element.
The mutation results in an improved
fitness score for S1. Mutation and
crossover continue until a termination
condition is satisfied. If the third
generation is terminal, then the final
solution is S2.

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Solution for Clustering
If S2 (3.0, 2.0) and (3.0, 5.0) is the final solution,
then computing the distances between S2 and
the following points are:
Instances 1, 3 and 5 forming one cluster and
instances 2 and 6 forming second cluster, and
instance 4 can be in either clusters.
Cluster 1 center (3.0, 2.0)
Instance X Y
1.0 1.5
2.0 1.5
3.0 2.5
Cluster 2 center (3.0, 5.0)
Instance X Y
1.0 4.5
2.0 3.5
5.0 6.0

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General considerations for GA
• GA are designed to find globally optimized
solutions.
• The fitness function determines the
computation complexity of a genetic
algorithm.
• GA explain their results to the extent that
the fitness function is understandable.
• Transforming the data to a form suitable
for a genetic algorithm can be a challenge.

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Choosing a data mining technique
Given a set of data containing attributes and
values to be mined together with information
about the nature of the data and the problem to
be solved, determine an appropriate data
mining technique.

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Considerations for choosing data
mining techniques
• Is learning supervised or unsupervised?
• Do we require a clear explanation about the
relationships present in the data?
• Is there one set of input attributes and one set of
output attributes or can attributes interact with one
another in several ways?
• Is the input data categorical, numeric, or a
combination of both?
• If learning is supervised, is there one output attribute
or are there several output attributes? Are the output
attribute(s) categorical or numeric?

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Behavior of different data mining techniques
1. Neural networks is black-box structured, and is a poor
choice if an explanation about what has been learned is
required.
2. Association rule is a best choice when attributes are
allowed to play multiple roles in the data mining
process.
3. Decision trees can determine attributes most predictive
of class membership.
4. Neural networks and clustering assume attributes to be
of equal importance.
5. Neural networks tend to outperform other models when
a wealth of noisy data are present.
6. Algorithms for building decision trees typically execute
faster than neural network or genetic learning.
7. Genetic algorithms is typically used for problems that
cannot be solved with traditional techniques.

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Review question 10
Given the following training data set
Training instance Income range Credit card insurance Sex Age
1 30-40k Yes Male 30-39
2 30-40k No Female 40-49
3 50-60k No Female 30-39
4 20-30k No Female 50-59
5 20-30k No Male 20-29
6 30-40k No Male 40-49
Describe the steps needed to apply unsupervised
genetic learning to cluster the instances of the credit
card promotion database.

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Tutorial Question 10Given the following training data set
Training instance Income range Credit card insurance Sex Age
1 30-40k Yes Male 30-39
2 30-40k No Female 40-49
3 50-60k No Female 30-39
4 20-30k No Female 50-59
5 20-30k No Male 20-29
6 30-40k No Male 40-49
After transforming the input data into numeric such as yes=1, no=2, male=1, female=2,
20-29=1, 30-39=2, 40-49=3, 50-59=4, 20-30k=1, 30-40k=2, 40-50k=3, 50-60k=4, the
training data set becomes:
T(1)=(2,1,1,2)
T(2)=(2,2,2,3)
T(3)=(4,2,2,2)
T(4)=(1,2,2,4)
T(5)=(1,2,1,1)
T(6)=(2,2,1,3)
Assume there are two set of initial population for two clusters as:
Solution 1 of 2 clusters centers: K1(1,1,1,1), (4,2,2,4)
Solution 2 of 2 clusters centers: K2(4,4,4,4), (2,2,1,1)
Choose the best solution based on their fitness function score by use of unsupervised
genetic learning.

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Reading assignment
“Data Mining: A Tutorial-based Primer” by
Richard J Roiger and Michael W. Geatz,
published by Person Education in 2003,
pp.89-101.