Title of Thesis

Advancements in Genetic Programming for Data Classification

Author(s)

Hajira Jabeen

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.

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1.1 Introduction
1.2 Classification and Evolutionary Algorithms
1.3 Genetic Programming for Classification
1.4 Research Goals and Objectives
1.5 Research Contributions
1.6 Structure of the Thesis

2.1 Introduction
2.2 Related Work
2.3 GP Evolution
2.4 Classification using GP
2.5 Fitness Function for Classification
2.6 Multiobjective Genetic Programming
2.7 Strengths and Weaknesses of GP Classification
2.8 Possible Solutions
2.9 Conclusion

3.1 Introduction
3.2 Related Work
3.3 Proposed DepthLimited Crossover
3.4 Results
3.5 Conclusion

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4.1 Introduction
4.2 Related Work
4.3 Proposed Hybrid GPSO
4.4 Results
4.5 Conclusion

5

5.1 Introduction
5.2 Related Work
5.3 Proposed Two Layered Approach
5.4 Results
5.5 Conclusion

6

6.1 Introduction
6.2 Related Work
6.3 Proposed Binary Encoding
6.4 Results
6.5 Conclusion

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7.1 Taxonomy of Classification using GP
7.2 DepthLimited Crossover
7.3 PSO based Optimization
7.4 Mixed Type Data Classification
7.5 Binary Encoding Based Method
7.6 Addition to Existing Literature
7.7 Future Research

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