الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
An attempt has been made to draw on immunological metaphors to build an Artificial Immune System (AIS) that can be applied to the area of Scheduling. The distinctive feature of the AIS is its ability to provide robust solutions.
Based on the research undertaken in this thesis to evaluate the existing AIS principles, models and applications, an algorithm applicable to Scheduling was built. This algorithm was based on the Clonal Selection theory. The algorithm has been implemented in Java and tested successfully for solving 7 real functions optimization. The results shown great performance in compare with all major AIS algorithms applied for the same problems.
Thereafter, a Python implementation was created to test the viability of this algorithm on university examination timetabling problems. The test comprised of evaluating 12 university examination timetabling problems with the new AIS model using the dimensions of optimality and robustness. Extensive testing revealed that the new AIS model was very competitive to other published techniques for the same problems in both terms of optimality and robustness.
Finally, a few areas for future research were identified to improve the optimality of the algorithm, such as, adding the aspect of ’memory’ and continuing to keep tabs on the advancements in the field of AIS.
Two publications have been prepared as a result of the research in this thesis. The first is titled “An Improved Version of opt-aiNet Algorithm (I-opt-aiNet) for Function Optimization”; and the second paper is titled “Applying Immune Algorithm I-opt-aiNet for University Examination Timetabling Problems”.