• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.389-394
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• 2014

Two evolutionary gait generation methods for Cartesian and Joint coordinates space are compared to develop a fast locomotion for quadruped robots. GA(Genetic Algorithm) based approaches seek to optimize a pre-selected set of parameters for the locus of paw and initial position in cartesian coordinates space. GP(Genetic Programming) based technique generate few joint trajectories using symbolic regression in joint coordinates space as a form of polynomials. Optimization for two proposed methods are executed using Webots simulation for the quadruped robot which is built by Bioloid. Furthermore, simulation results for two proposed methods are analysed in terms of different coordinate spaces.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1355-1360
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• 2005

In this paper, we approach the problem of image filter design automation using a kind of intrinsic evolvable hardware architecture. For the purpose of implementing the intrinsic evolution process in a common FPGA chip and evolving a complicated digital circuit system-image filter, the design automation system employs the reconfigurable circuit architecture as the reconfigurable component of the EHW. The reconfigurable circuit architecture is inspired by the Cartesian Genetic Programming and the functional level evolution. To increase the speed of the hardware evolution, the whole evolvable hardware system which consists of evolution algorithm unit, fitness value calculation unit and reconfigurable unit are implemented by a commercial FPGA chip. The Celoxica RC1000 card which is fitted with a Xilinx Virtex xcv2000E FPGA chip is employed as the experiment platform. As the result, we conclude the terms of the synthesis report of the image filter design automation system and hardware evolution speed in the Celoxica RC1000 card. The evolved image filter is also compared with the conventional image filter form the point of filtered image quality.