Meihua Yang
Papers
1
Total Citations
8
H-Index
1
About
Meihua Yang is a researcher specializing in evolutionary computation, rule-based decision-making systems, and genetic network programming. Her work focuses on developing intelligent algorithms that can autonomously learn and adapt rules for complex decision-making tasks. In her most-cited paper, "Niching genetic network programming with rule accumulation for decision making: An evolutionary rule-based approach" (2018), Yang introduced a novel method that combines niching techniques with genetic network programming to enhance rule diversity and accumulation. This approach allows for more robust and efficient decision-making in dynamic environments, demonstrating significant improvements over traditional evolutionary methods. With over 8 citations, this work has contributed to advancing the field of evolutionary rule-based systems, offering practical solutions for applications in robotics, control systems, and artificial intelligence. Yang's research bridges the gap between evolutionary algorithms and real-world decision-making challenges, making her a notable contributor to the development of adaptive, self-learning systems. Her work continues to inspire further exploration into hybrid evolutionary techniques for complex problem-solving.
Research Focus
Key Achievements
Top Papers
- 1