【摘 要】
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Though Lamarckian genetic algorithm has demonstrated excellent performance in terms of protein-ligand docking problems,it can not memorize the evaluated solutions that it has accessed,rendering it eff
【出 处】
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第二届中国计算机学会生物信息学会议
论文部分内容阅读
Though Lamarckian genetic algorithm has demonstrated excellent performance in terms of protein-ligand docking problems,it can not memorize the evaluated solutions that it has accessed,rendering it effort-consuming to discover some promising solutions.This paper illustrates a novel and robust optimization algorithm(HIGA)based on Lamarckian genetic algorithm(LGA)for solving the flexible protein-ligand docking problems with an aim to overcome the above-mentioned drawback.A running history information guided model is applied in the method,which makes it more efficient to find the lowest energy of protein-ligand docking.We evaluate the performance in the aspects of lowest energy and highest accuracy of HIGA in comparison with GA,LGA,SODOCK,and ABC,the results of which indicate that HIGA outperforms other search algorithms.
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