Allows users to perform protein-ligand docking. HIGA is a running history information guided genetic algorithm. It starts its analysis by a random population initialization followed by a CE crossover, an ED mutation, a BSP tree, a local search, a selection and, finally a fitness evaluation. This method represents an extension of LGA-based algorithm modified by adding binary space partitioning (BSP), ED mutation, and CE crossover.
Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, and School of Computer Science and Engineering, Northeastern University, Shenyang, China
HIGA funding source(s)
Supported by the National Natural Science Foundation Program of China (61772124), the State Key Program of National Natural Science of China (61332014), the Fundamental Research Funds for the Central Universities under Grant 150402002 and Grant 150404008, and the Peak Discipline Construction of Computer Science and Technology under Grant 021900218210.