Abstract:
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 thesis are:
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 transformation.
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.
