Benjamin N. Passow

About

Benjamin N. Passow is a leading figure in evolutionary computation and robotics, whose work bridges the gap between theoretical algorithms and real-world autonomous systems. His primary research focuses on evolutionary optimization, particularly the development of high-performance yet elegantly simple algorithms, and the robust application of genetic algorithms (GAs) to control systems. Passow’s most influential contribution is the "Re-sampled inheritance search" (2013, 38 citations), a novel approach that demonstrates how simplicity can yield exceptional performance in complex optimization tasks. This work has become a touchstone for researchers seeking efficient, scalable evolutionary methods. His impact is further underscored by pioneering research on robustness analysis for evolutionary controller tuning (2009, 15 citations), where he directly applied GAs to evolve heading and altitude controllers for a small lightweight helicopter. Crucially, Passow evaluated these controllers on the real flying robot, bypassing artificial simulations to validate performance under genuine physical conditions. This hands-on, real-world testing approach has inspired a generation of roboticists to prioritize practical robustness over theoretical consistency. Passow’s work continues to shape how evolutionary algorithms are deployed in autonomous systems, making him a key figure in the advancement of intelligent, adaptive robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Re-sampled inheritance search: high performance despite the simplicity

   38 citations · 2013
2. 2
   Robustness analysis of evolutionary controller tuning using real systems

   15 citations · 2009

Key Collaborators

Contact & Links