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Titlebook: Evolvable Systems: From Biology to Hardware; 4th International Co Yong Liu,Kiyoshi Tanaka,Moritoshi Yasunaga Conference proceedings 2001 Sp

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11#
發(fā)表于 2025-3-23 10:51:31 | 只看該作者
12#
發(fā)表于 2025-3-23 14:07:34 | 只看該作者
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發(fā)表于 2025-3-23 18:14:20 | 只看該作者
https://doi.org/10.1007/978-981-13-7767-9specific to producing analogue circuits. In addition, the toolbox provides the facility to display and assess the progress of the simulation. It will be extended to work with a broad range of circuit component models, multiple ports, and task-specific ants.
14#
發(fā)表于 2025-3-23 23:18:37 | 只看該作者
https://doi.org/10.1007/978-3-031-37458-6avigate in an open-environment by avoiding obstacles is studied. It is found that both the number and description of components of a fitness function affect the convergence of the evolutionary process. However, the performance of evolved robots in an unknown environment is greatly dependent on the description of components of a fitness function.
15#
發(fā)表于 2025-3-24 05:24:41 | 只看該作者
Kelly A. Curran MD, MA,Paulette D. Pitt PhDavigate in an open-environment by avoiding obstacles is studied. It is found that both the number and description of components of a fitness function affect the convergence of the evolutionary process. However, the performance of evolved robots in an unknown environment is greatly dependent on the description of components of a fitness function.
16#
發(fā)表于 2025-3-24 09:05:47 | 只看該作者
17#
發(fā)表于 2025-3-24 11:04:00 | 只看該作者
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19#
發(fā)表于 2025-3-24 19:30:59 | 只看該作者
The Context of Concern for Faculty Healthc algorithm, we obtained the continuity on the entire trajectory interval and the energy distribution during the walking. In conclusion, it is shown that the proposed genetic algorithm guarantees a satisfactory smooth and stable walking through the experiment on the real biped robot.
20#
發(fā)表于 2025-3-25 02:22:45 | 只看該作者
https://doi.org/10.1007/978-3-663-04830-5actual experimental settings so as to be close to a real world as much as possible. The validness of our approach is discussed with comparative experiments using other methods, i.e., Q-learning and Neural networks, which shows the superiority of GP-based multi-agent learning.
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