A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

The convolutional neural network (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, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our method is available at https://github.com/sg-nm/cgp-cn

Aerospace medicine and Biology: A continuing bibliography with indexes, supplement 177

This bibliography lists 112 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1978

Remote systems development

Potential space missions of the nineties and the next century require that we look at the broad category of remote systems as an important means to achieve cost-effective operations, exploration and colonization objectives. This paper addresses such missions, which can use remote systems technology as the basis for identifying required capabilities which must be provided. The relationship of the space-based tasks to similar tasks required for terrestrial applications is discussed. The development status of the required technology is assessed and major issues which must be addressed to meet future requirements are identified. This includes the proper mix of humans and machines, from pure teleoperation to full autonomy; the degree of worksite compatibility for a robotic system; and the required design parameters, such as degrees-of-freedom. Methods for resolution are discussed including analysis, graphical simulation and the use of laboratory test beds. Grumman experience in the application of these techniques to a variety of design issues are presented utilizing the Telerobotics Development Laboratory which includes a 17-DOF robot system, a variety of sensing elements, Deneb/IRIS graphics workstations and control stations. The use of task/worksite mockups, remote system development test beds and graphical analysis are discussed with examples of typical results such as estimates of task times, task feasibility and resulting recommendations for design changes. The relationship of this experience and lessons-learned to future development of remote systems is also discussed