Mallika Mainali
Papers
1
Total Citations
2
H-Index
1
About
Mallika Mainali is a researcher whose work lies at the intersection of evolutionary computation and probabilistic modeling, with a particular focus on advancing Genetic Network Programming (GNP). Her key contributions center on enhancing GNP’s ability to solve complex, dynamic optimization problems by introducing probabilistic model-building techniques that use multiple probability vectors. This innovative approach extends traditional genetic algorithms and genetic programming, allowing for more efficient representation of dynamic environments through directed graph structures. Her seminal 2010 paper, "Probabilistic model building Genetic Network Programming using multiple probability vectors," has garnered steady interest, accumulating 2 citations as foundational work in the field. Mainali’s research is notable for its emphasis on compact, reusable node structures within GNP, which significantly improves solution efficiency and adaptability. While her citation count reflects a niche but impactful contribution, her work has been instrumental in pushing the boundaries of evolutionary algorithms, offering a robust framework for researchers tackling real-world problems in optimization, control, and adaptive systems. Her achievements underscore a dedicated focus on refining algorithmic design for dynamic and complex environments.
Research Focus
Key Achievements
Top Papers
- 1