1,954 research outputs found
Exploiting Tournament Selection for Efficient Parallel Genetic Programming
Genetic Programming (GP) is a computationally intensive technique which is
naturally parallel in nature. Consequently, many attempts have been made to
improve its run-time from exploiting highly parallel hardware such as GPUs.
However, a second methodology of improving the speed of GP is through
efficiency techniques such as subtree caching. However achieving parallel
performance and efficiency is a difficult task. This paper will demonstrate an
efficiency saving for GP compatible with the harnessing of parallel CPU
hardware by exploiting tournament selection. Significant efficiency savings are
demonstrated whilst retaining the capability of a high performance parallel
implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved
with a peak rate of 96 billion GPop/s for classification type problems
Low pressure pneumatic servomechanisms
Tests on a continuous action low pressure electro-pneumatic linear motion servomechanism are described. Data from individual tests on the components of this system, including the servo valve and the actuator, is used for analogue computer simulations. The simulations are shown to model the experimental system over a wide range of conditions. A simple linear equation is shown to model many aspects of the behaviour of the system. This equation is presented in a form which enables the importance of many system parameters to be easily assessed.
An all pneumatic linear-motion servo using discrete fluidic components is described. This system uses pulse-width modulated signals to control on/off valves. Tests on components of the system, including beam deflection proportional amplifiers, Schmitt triggers, valves and a fluidic displacement sensor and also on the complete servo are described.
Shortcomings in currently available components are shown
to limit the performance of both systems, and modifications necessary to improve their closed loop behaviour are proposed
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