Optimization of walking motion by real type genetic algorithm with floor reaction force as evaluation function

2018 ◽  
Vol 2018 (0) ◽  
pp. G2300002
Author(s):  
Koki NAKANO ◽  
Makishi NAKAYAMA
Keyword(s):  
Genetic Algorithm ◽  
Reaction Force ◽  
Evaluation Function ◽  
Walking Motion ◽  
Floor Reaction Force ◽  
Real Type

scholarly journals Estimate of Floor Reaction Force Vector Using Foot-Pressure Sensors

2008 ◽  
Vol 74 (739) ◽  
pp. 749-751 ◽  
Author(s):  
Kazuto MIYAWAKI ◽  
Takehiro IWAMI ◽  
Goro OBINATA ◽  
Yoichi SHIMADA
Keyword(s):  
Reaction Force ◽  
Pressure Sensors ◽  
Force Vector ◽  
Foot Pressure ◽  
Floor Reaction Force

J1040103 Influence of Posture on the Muscle Activity and Floor Reaction Force in Reach Movement by Healthy Subjects

2015 ◽  
Vol 2015 (0) ◽  
pp. _J1040103--_J1040103-
Author(s):  
Shinnosuke ATARASHI ◽  
Atsuhiko SHINTANI ◽  
Ryo YONETSU ◽  
Chihiro NAKAGAWA ◽  
Tomohiro ITO
Keyword(s):  
Muscle Activity ◽  
Healthy Subjects ◽  
Reaction Force ◽  
Floor Reaction Force

Multiobjective Optimization of Hybrid Electrical Vehicle Powertrain Mounting System Using Hybrid Genetic Algorithm

2011 ◽  
Vol 87 ◽  
pp. 30-37 ◽  
Author(s):  
Jian Feng He ◽  
Xiao Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Multiobjective Optimization ◽  
Energy Distribution ◽  
Reaction Force ◽  
Hybrid Genetic Algorithm ◽  
Electrical Vehicle ◽  
Distribution Matrix ◽  
Vehicle Powertrain ◽  
Hybrid Electrical Vehicle

Powertrain mounting system of a Hybrid Electrical Vehicle (HEV) is analyzed and researched, the expression of energy distribution matrix and that of mounting reaction force are derived, and mathematical model of the system is established in Matlab. Correctness of the model established is tested and verified through model establishing for simulation and calculation in ADAMS. Features of Hybrid Genetic Algorithm (HGA) for multiobjective optimization are analyzed and researched, model for calculation of multiobjective optimization using Hybrid Genetic Algorithm is established, targets for optimization of the system are determined, and optimization is executed based on the mounting stiffness parameters. The result that the system is optimized apparently by Hybrid Genetic Algorithm is revealed through contrast of the energy distribution matrix and mounting reaction force of pre and post-optimization.

Life Algorithm: Its Concept and Application

1995 ◽  
Author(s):  
Shuichi Fukuda ◽  
Fumikazu Konishi
Keyword(s):  
Genetic Algorithm ◽  
Solar Cell ◽  
Power Plant ◽  
Autonomous System ◽  
Cell System ◽  
Evaluation Function ◽  
Changing Environment ◽  
Plant Operation ◽  
A Genome ◽  
Power Plant Operation

Abstract Recently an autonomous system that meets the growing demand for solving a problem in adiversifying environment has been desired, in order to secure more robustness and flexibility. As a first step towards establishing such an autonomous system, we developed Life Algorithm (LA). Life Algorithm is fundamentally an extension of Genetic Algorithm (GA). While the conventional GA approach is based on genes and is homogeneous in its essence, LA is something more like a genome and realizes more diversity. A group of genes with different evaluation function is considered and these constitute agents. Thus, more individuality and more interaction between these agents can be taken into account and more robustness to cope with the widely and quickly changing environment can be realized. This paper demonstrates the usefulness of an LA approach by illustrating the example of power plant operation through simulation and an experiment of a solar cell system.

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