Dr. Syed Qamar Askari

Assistant Professor
‏‏‏PhD, Computer Science
National University of Computer and Emerging Sciences, Lahore


Qamar Askari received the M.Sc. degree in computer science from the University of Punjab, Lahore and degrees of MS and PhD in computer science from National University and College of Emerging Sciences (NUCES), Lahore. He is actively researching in the area of Computational Intelligence especially swarm intelligence, evolutionary computation, and utilization of meta-heuristics for machine learning. He has published and worked on several novel meta-heuristics.

He is currently serving as an Assistant Professor of Computer Science at GIFT University, Gujranwala. He teaches different subjects from the area of artificial intelligence and theoretical computer sciences including artificial intelligence, machine learning, data structures and algorithms, and theory of automata. He is leading a research group at GIFT aimed to explore and contribute in the area of computational intelligence.

Selected Journal Publications

  • Askari, Q. and Younas, I., 2021. Improved political optimizer for complex landscapes and engineering optimization problems. Expert Systems with Applications, 182, p.115178.
  • Askari, Q., Younas, I. and Saeed, M., 2021. Emphasizing the importance of shift invariance in metaheuristics by using whale optimization algorithm as a test bed. Soft Computing, 25(22), pp.14209-14225.
  • Askari, Q. and Younas, I., 2021. Political Optimizer Based Feedforward Neural Network for Classification and Function Approximation. Neural Processing Letters, 53(1), pp.429-458. Askari, Q., Younas, I., & Saeed, M. (2020). Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems, 105709.
  • Askari, Q., Saeed, M., & Younas, I. (2020). Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Systems with Applications, 113702.
  • Askari, Q., Younas, I., & Saeed, M. (2020, July). Critical evaluation of sine cosine algorithm and a few recommendations. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 319-320).