الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The thesis tries to emphasize the power of genetic
algorithm (GA) in dealing with symbolic data and
supervised learning. Genetic algorithm tries to find all
perfect solutions hidden in a hyper-space of a number of
attributes.
The thesis tries to find out an algorithm based on the
genetic algorithm that solves for finding out hidden
different concepts inside a number of given examples in a
noisy environment.
A set of derived Boolean and fuzzy attributes is
defined to a number of examples belonging to a single table
in a database file. The attributes then get their values from
the database table using SQL SELECT statements. Then, a
genetic algorithm and a classifier system try to extract
hidden common concepts between these attributes.
The concept structure is represented using a prefix
Boolean expression grammar. This representation method
can discover new hidden unexpected knowledge inside the
examples. Both of the genetic algorithm and classifier
system needs to be varied to cope with the new
representation structure.In order to find a number of different solutions, the
genetic algorithm has been modified to punish trivial, alike
concepts. The punishment occurs in such a manner that
enables only a single concept to maintain its power while
the other concepts lose some of their fitness to encourage
searching in different directions. The superset concepts also
are punished when there are perfect subset concepts. That
enables getting more specific concepts rather than general
ones.The two proposed methods try to solve for two real
examples (4-bit shift register and student-marks). Then, a
source of noise is applied on both examples to simulate the
effect of odd cases and noisy data on the input data and the
methods run again to extract concepts under noise.Keywords
Artificial Intelligence, Machine Learning, Concept
Learning, Data Mining, Genetic Algorithm, Classifier
Learning System, Supervised Learning, Fuzzy Logic.