基于复数编码遗传算法的竞争性协进化策略  

Competitive co-evolution strategy based on genetic algorithm with complex-valued encoding

作　　者：谭冠政[1] 刘良敏[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《中南大学学报（自然科学版）》2005年第3期475-480,共6页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(50275150);中国科学院机器人学开放研究实验室基金资助项目(RL200002)

摘　　要：将基于复数编码的遗传算法引入竞争性协进化的理论研究中,提出一种竞争性协进化的新策略,即在仿真实验中,采用2个基于神经网络结构控制的移动机器人,并将它们投入到一个陌生的环境中。其中,一个机器人扮演猎手,另一个扮演猎物,猎手对猎物进行捕捉,最终得到每一代的最好猎手机器人和最好猎物机器人以及它们的适应度曲线。在这个竞争性协进化系统中,基于复数编码的遗传算法主要用于对机器人控制系统的神经网络进行进化。计算机仿真结果表明,与基本遗传算法相比,基于复数编码的遗传算法具有更强的进化能力。The genetic algorithm with complex-valued encoding was introduced to the research of competitive co-evolution theory, and a new strategy for competitive co-evolution was proposed. In the simulation experiments, two mobile robots with neural networks control structure were put into an unknown simulation environment. One of them played the part of hunter and the other played the prey; the hunter always tried to catch the prey. The best hunter and the best prey of every generation were obtained from the experiment, including the fitness curves of them. The genetic algorithm with complex-valued encoding was mainly used to evolve the neural networks of controller of robots. The experiment results show that the genetic algorithm with complex-valued encoding has stronger evolutionary capacity compared with the general genetic algorithm.

关 键 词：进化机器人 竞争性协进化 遗传算法 神经网络 连接权 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]