• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.1
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• pp.158-166
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• 2003

The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.4
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• pp.357-365
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• 2009

A general analysis of a microstrip-to-vertically mounted slotline(VMS) coupler is presented with a view to developing an equivalent circuit, and the efficient evaluation of the related circuit element values. Based on this theory, the effects of frequency and structure parameters such as aperture length and VMS width on the characteristics of the coupler are studied. In order to check the validity of the proposed analysis and design theory, a C-band linearly tapered slot antenna fed by an aperture-coupled back-to-back microstripline-to- VMS coupling structure is optimally designed using a hybrid genetic algorithm. Moreover, the computed characteristics from the network analysis is compared to the measurement and simulation results. The obtained results fully validate the efficiency and accuracy of the proposed network model.