Monika Gope

Khulna University of Engineering and Technology

Papers

Total Citations

H-Index

About

Monika Gope’s research lies at the intersection of computational intelligence, multi-objective optimization, and fuzzy logic control systems. Her core contributions focus on enhancing the interpretability and precision of fuzzy rule-based systems through evolutionary algorithms. In her most cited work, she applied the fast elitist non-dominated sorting genetic algorithm (NSGA-II) to develop smartly tuned fuzzy logic controllers, achieving a finer trade-off between model accuracy and rule transparency—a critical challenge in complex combinatorial optimization. Building on this, she proposed a hybrid method that integrates neural networks with genetic algorithms to optimize fuzzy neural network controllers, using clustering techniques to streamline rule generation. Though her citation counts are currently modest—with her top paper garnering 3 citations—her work demonstrates a systematic approach to advancing intelligent control systems. Gope’s research is particularly relevant for students and engineers exploring evolutionary multi-objective optimization and its application to real-world control problems, offering a foundation for further innovation in adaptive and interpretable fuzzy systems.