226,029 research outputs found

    Optimum design of a probe fed dual frequency patch antenna using genetic algorithm

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    Abstract: Recent research has concentrated on different designs in order to increase the bandwidth of patch antennas and thus improve functionality of wireless communication systems. An alternative approach as shown in this paper is to design a matched probe fed rectangular patch antenna which can operate at both dual frequency (1.9 GHz and 2.4 GHz) and dual polarisation. In this design there are four variables, the two dimensions of the rectangular patch, ‘a ’ and ‘b ’ and position of the probe feed ‘Xp ’ and ‘YP’. As there is not a unique solution Genetic Algorithm (GA) was applied using two objective functions for the return loss at each frequency. The antenna was then modelled using AWR software and the predicted and practical results are shown to be in good agreement. Key Words: Genetic algorithm (GA), dual frequency, dual polarisation, probe fed patch antenn

    An alternative measurement of the entropy evolution of a genetic algorithm

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    This is an electronic version of the paper presented at The European Simulation and Modelling Conference (ESM), held in Leicester (United Kingdom) on 2009In a genetic algorithm, fluctuations of the entropy of a genome over time are interpreted as fluctuations of the information that the genome’s organism is storing about its environment, being this reflected in more complex organisms. The computation of this entropy presents technical problems due to the small population sizes used in practice. In this work we propose and test an alternative way of measuring the entropy variation in a population by means of algorithmic information theory, where the entropy variation between two generational steps is the Kolmogorov complexity of the first step conditioned to the second one. We also report experimental differences in entropy evolution between systems in which sexual reproduction is present or absent.This work has been partially sponsored by MICINN, project TIN2008-02081/TIN and by DGUI CAM/UAM, project CCG08-UAM/TIC-4425

    Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm

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    We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA). Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorting to any periodicity and similarity assumptions. Based on numerical experiments, genetic optimizations are improved by considering alternative mutation, crossover, and elitism mechanisms. We show that the developed optimization environment based on genetic algorithms and MLFMA provides efficient and effective optimizations of antenna excitations, which cannot be obtained with array-factor approaches, even for relatively simple arrays with identical elements

    Natural Selection at Work: An Accelerated Evolutionary Computing Approach to Predictive Model Selection

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    We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency
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