CEC Theses and Dissertations


Finding a Fitness Function to be Used with Genetic Algorithms to Solve a Protein Folding Problem: The ab initio Prediction of a Protein Using Torsion Angles

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Graduate School of Computer and Information Sciences


Michael J. Laszlo

Committee Member

Sumitra Mukherjee

Committee Member

Lee Leitner


This dissertation shows that the ab initio prediction of a protein using torsion angles will work using the correct fitness function. It shows that work can be done on a high-end workstation using a small model of a protein. It was based on the previous work of Dr. Steffen Schulze-Kremer who received limited success with a faculty fitness function and a massively parallel system. The purpose of this work was to not only find the solution but to demonstrate how our rapidly advancing technology will permit this type of research to be moved from the costly parallel systems, nuclear magnetic resonance, and x-ray crystallography to a less costly microcomputer system. In order to accomplish this, the code was run with Microsoft's Visual C++ (version 6) on Intel systems running at 220 MHz, 550 MHz, and 700 MHz with 40 MB, 512 MB, and 256 MB of memory. The results of this work will pave the way for further research in this area on less costly hardware.

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