The Institute of Informatics for Graduate Studies at the University of Information Technology and Communications discussed a doctoral dissertation submitted by student Abbas Mahmoud Ahmed in the field of Computer Science, entitled:

“Evolutionary Optimization Algorithms for Gene Selection and Classification of Common Diseases: LPB Algorithm – A Case Study”

The dissertation aimed to develop more efficient methods to address the challenges facing traditional optimization algorithms, particularly early convergence problems, limited solution diversity, and achieving a balance between exploration and exploitation. The study was based on expanding the capabilities of the performance-based learning (LPB) algorithm to become an advanced optimization model by introducing two new versions: the modified LPB (mLPB) and the adaptive LPB (aLPB). This contributes to improving the performance of gene selection and classification processes for common diseases.

The study results demonstrated the effectiveness of the developed models in addressing critical optimization problems and enhancing classification accuracy. This opens new horizons for employing computational intelligence techniques in medical and biological applications. Following the dissertation defense by the chair and members of the examining committee, the student was awarded a PhD in Computer Science with a grade of “Very Good.”