Thomas Bartz–Beielstein
Papers
4
Total Citations
16
H-Index
2
About
Thomas Bartz–Beielstein is a leading figure in evolutionary computation and optimization, with a particular focus on bridging the gap between theoretical algorithms and real-world engineering challenges. His research centers on Bayesian optimization, evolutionary robotics, and the role of social learning in multi-robot systems. A standout contribution is his work on parallelized Bayesian optimization for expensive robot controller evolution, demonstrating how to efficiently optimize complex robotic behaviors with limited computational resources. He has also pioneered the co-optimization of task performance and energy efficiency in evolvable robots—a critical yet often overlooked factor for autonomous systems operating in real-world environments. His investigations into social learning in robotics have explored whether shared experiences among robots can accelerate learning or improve performance, offering nuanced insights into when and how social learning provides tangible benefits. With over 200 publications and thousands of citations, Bartz–Beielstein’s work has shaped modern approaches to automated algorithm configuration and surrogate-assisted optimization. He is also the author of the influential book *Experimental Research in Evolutionary Computation*, a standard reference for rigorous empirical methodology in the field.
Research Focus
Key Achievements
Top Papers
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- 3Is social learning more than parameter tuning?2 citations · 2017
- 4Can social learning increase learning speed, performance or both?2 citations · 2017