Hybrid Algorithm

Hybrid algorithms combine the strengths of different optimization or machine learning techniques to achieve superior performance compared to single-method approaches. Current research focuses on developing and applying these hybrid methods across diverse fields, including clustering, motion planning, and reinforcement learning, often integrating evolutionary algorithms, gradient descent, and local search heuristics within novel architectures. This interdisciplinary approach leads to improved accuracy, efficiency, and scalability in solving complex problems, impacting areas such as e-commerce fraud detection, robotics, and large-scale data analysis.