Title of Thesis
Advancements in Genetic Programming for Data Classification
Department of Computer Science / Fast National
University of Computer & Emerging Sciences, Islamabad
|Number of Pages|
|Keywords (Extracted from title, table of contents and
abstract of thesis)|
Contribution, Genetic, Transformation, Advancements, Experiments,
Data, Solutions, Programming
This thesis aims to
advance the state of the art in data classification using Genetic
programming(GP).GP is an evolutionary algorithm that has several
outstanding features making it ideal for complex problems like data
classification. However, it suffers from a few limitations that
reduce its significance. This thesis targets at proposing optimal
solutions to these GP limitations.The problems covered in this
1. Increase in GP tree complexity during evolution that results in
long training time.
2. Lack of convergence to a single (optimal) solution.
3. Lack of methodology to handle mixed data-type without type
4. Search of a better method for multi-class classification.
Through this work, we have proposed a method which achieves
significant reduction in bloat for classification task. Moreover, we
have presented a Particle Swarm Optimization based hybrid approach
to increase performance of GP evolved classifiers.The approach
offers better performance in less computational effort. Another
approach introduces a new two layered paradigm for mixed type data
classification with an added feature that uses data in its original
form instead of any transformation or pre-processing.The last but
not the least contribution is an efficient binary encoding method
for multi-class classification problems. The method involves smaller
number of GP evolutions, reducing the computation and suffers from
fewer conflicts yielding better results.
All of the proposed methods have been tested and our experiments
conclude the efficiency of proposed approaches.