Jochen Krause
Papers
1
Total Citations
10
H-Index
1
About
Jochen Krause is a researcher whose work bridges the fields of neural network optimization and evolutionary computation. His most-cited study, "Efficient Learning of Neural Networks with Evolutionary Algorithms" (2007), has garnered 10 citations and introduces a novel approach to training neural networks by leveraging evolutionary strategies to overcome the limitations of traditional gradient-based methods. This contribution is particularly notable for its focus on efficiency and scalability, offering a pathway to more robust learning in complex, non-convex optimization landscapes. Krause’s research addresses a critical challenge in machine learning: how to effectively train networks when backpropagation is infeasible or suboptimal. By integrating evolutionary algorithms, he has helped advance the understanding of neuroevolution, a field with applications in robotics, reinforcement learning, and adaptive systems. While his citation count reflects a focused, niche impact, his work serves as a foundational reference for researchers exploring alternative training paradigms. Krause’s contributions underscore the value of interdisciplinary approaches in artificial intelligence, making his research a key resource for students and scientists interested in the intersection of evolutionary biology and deep learning.
Research Focus
Key Achievements
Top Papers
- 1Efficient Learning of Neural Networks with Evolutionary Algorithms10 citations · 2007