Papers
8
Total Citations
109
H-Index
5
About
Amine Boumaza is a researcher whose work spans evolutionary robotics, swarm intelligence, and pattern recognition, with a particular focus on developing biologically inspired algorithms for autonomous systems. His early and most influential contributions center on "Dynamic Flies," a novel pattern recognition framework drawing on Parisian Evolution — a genetic algorithm paradigm in which populations of agents collectively encode solutions. Introduced in a pair of seminal papers from 2001 and 2002 (earning 39 and 26 citations respectively), this approach demonstrated compelling results in stereo sequence processing and real-time robotics applications, with subsequent work extending it to mobile robot sensor fusion. Boumaza's research evolved naturally toward distributed evolutionary robotics and swarm systems, where he has investigated how populations of robots can autonomously adapt and learn through embodied evolution — evolution occurring directly on deployed hardware rather than in simulation. His 2017 work on collaborative foraging and his studies comparing selection methods in on-line distributed evolutionary algorithms reflect a sustained commitment to understanding how evolutionary pressure shapes emergent swarm behavior. His 2018 contribution on diversity maintenance in robot swarms further underscores his interest in preventing premature convergence in decentralized systems. Collectively, his body of work bridges computational intelligence and autonomous robotics, offering meaningful insights for researchers designing adaptive, self-organizing multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Dynamic Flies: Using Real-Time Parisian Evolution in Robotics26 citations · 2001
- 3
- 4Comparison of Selection Methods in On-Line Distributed Evolutionary Robotics14 citations · 2014
- 5Mobile Robot Sensor Fusion Using Flies8 citations · 2003
- 6Block-synchronous Harmonic Control for Scalable Trajectory Planning3 citations · 2008
- 7
- 8