Evolutionary Computing and Data Classification: A Detailed Report

This report provides a comprehensive overview of the application of evolutionary computing in data classification. It begins with an introduction to the topic, followed by a detailed explanation of the methodology used, including the specific algorithms and datasets employed. The report includes a description of the experimental setup, a discussion of the results obtained, and an analysis of the performance of the different algorithms. The report also includes a discussion of the limitations of the approach and suggestions for future work. The report highlights the practical applications of evolutionary computing in the field of data science. The report also references relevant literature and includes a conclusion that summarizes the key findings and provides recommendations for future research. The report includes details about the coding and dataset used.

Assignment Title
Name & student no.
1 INTRODUCTION
Concise description of task asked and your
approach(es).
2 BACKGROUND RESEARCH
Evolutionary computing has been used in data
mining in many ways. Give a brief summary of some
examples you can find, connecting them to your
assignment perhaps too. Use clear referencing and
avoid including material directly unless carefully
indicated as from another source. Half to a whole
page expected.
3 EXPERIMENTATION
3.1 Data Set 1
Describe your basic algorithm so anyone who knows
about basic GAs could hopefully repeat what you
have done. Describe the representation, the
parameters, the fitness calculation, etc.
Present your results as averaged behaviour over
more than one run, eg (graph not of assignment):
Figure 1: Initial performance on dataset 1.
Show the effects of varying parameters and give
potential explanations as to why the
behaviour/performance changes. Eg vary the
mutation rate or population size.
3.2 Data Set 2
If not in the previous section, this should include
how you allow generalisation in the representation.
Again, show results from varying parameters/aspects
and explain them. The use of evolution to learn how
many rules to use would be good to include here
and/or other modifications.
3.3 Data Set 3 (and UCI data)
Clearly describe how you changed the representation
to deal with real-valued data. And then present
results from its use, with graphs and explanations.
For the very keen, it would be great to see
comparative performance on the UCI dataset too
and/or the use of another representation scheme, eg,
evolving neural networks, or use of Weka, etc.
4 CONCLUSIONS
Concise summary of what you found and learned.
Identification of ways you might do things
differently next time, and why.
REFERENCES
Bernado Mansilla, E. & Garrell, J. (2003) Accuracy-Based
Learning Classifier Systems: Models, Analysis and
Applications to Classification Tasks. Evolutionary
Computation 11(3): 209-238.
And please include either a link to your source
code so that more than one person can access it
over the next few months or as a full listing in an
Appendix. The second marker and external
examiner need to be able to see what you actually
produced.