Paul Agarwal

U.S. Army Space and Missile Defense Command

Papers

Total Citations

H-Index

About

Paul Agarwal’s research centers on the intersection of evolutionary computation (EC) and high-performance computing (HPC), where he has pioneered methods to accelerate global optimization for complex, non-linear problems. His major contributions include developing scalable frameworks that leverage PC clusters to overcome the computational intensity of evolutionary algorithms, enabling more efficient solution of real-world engineering and scientific challenges. His most-cited work, “High Performance Evolutionary Computing” (2006, 4 citations), introduces techniques to enhance EC’s fitness evolution through optimized selection, recombination, and mutation processes on parallel architectures. A companion paper, “High Performance Evolutionary Computation” (2006, 2 citations), further addresses cost and performance barriers by demonstrating how commodity clusters can democratize access to powerful optimization tools. Though his citation counts are modest, Agarwal’s early advocacy for accessible, high-performance EC platforms has influenced subsequent research in distributed evolutionary systems. His work is notable for bridging theoretical algorithm design with practical HPC implementation, offering a blueprint for researchers tackling computationally intensive optimization tasks without specialized supercomputing resources.