Radovan Simic
Papers
1
Total Citations
3
H-Index
1
About
Radovan Simic is a researcher focused on the intersection of robotics, optimization, and artificial intelligence, with a particular emphasis on multi-agent systems and evolutionary computation. His most cited work, "Optimization of Multi-Robot Sumo Fight Simulation by a Genetic Algorithm to Identify Dominant Robot Capabilities" (2019), introduces a novel computational model where multiple sumo fighters physically interact to push opponents out of an arena. By applying a genetic algorithm (GA) to optimize robot capabilities, Simic demonstrates how evolutionary techniques can systematically identify dominant traits in competitive multi-robot environments. This research contributes to the broader understanding of autonomous decision-making and adaptive strategies in robotics. Though his citation count is modest, with 3 citations for this paper, the work represents a foundational step in applying genetic algorithms to physical multi-agent simulations, offering insights that could inform future studies in swarm robotics and competitive AI. Simic’s approach highlights the potential for GA-driven optimization to solve complex, real-world interaction problems, making his research valuable for students and researchers exploring evolutionary robotics and multi-robot coordination.
Research Focus
Key Achievements
Top Papers
- 1