Drum rhythm automatic construction is an important step towards the design of systems which automatically compose music. This work describes a novel mechanism that allows a system, namely the evoDrummer, to create nov...
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In this paper, a genetic Algorithm (GA) design methodology is presented for the synthesis of doubly periodic all-dielectric Frequency Selective Surfaces (DFSS). The GA is used to optimize the dielectric constants of t...
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The paper aims to present a method to increase runway capacity by planning optimal sequences with genetic algorithms using enhanced heuristics and parameter estimation with neural networks. A Timed Stochastic Coloured...
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ISBN:
(纸本)9781604232271
The paper aims to present a method to increase runway capacity by planning optimal sequences with genetic algorithms using enhanced heuristics and parameter estimation with neural networks. A Timed Stochastic Coloured Petri Net model of the runway is used to generate pre-optimized sequences as the initial population of the genetic algorithm. The performance of the algorithm has been investigated by simulation for the case of arrival peak and arrival and departure peak and 20% saving in sequence completion time could be achieved. With use of enhanced heuristics computational time decreased by 10%.
In available Bridge Management System (BMS) there are different models to optimize the funds that each Agency dedicates to those matters. The paper presents a model developed for the BMS of the Chiapas State in Mexico...
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ISBN:
(纸本)0415403154
In available Bridge Management System (BMS) there are different models to optimize the funds that each Agency dedicates to those matters. The paper presents a model developed for the BMS of the Chiapas State in Mexico, using a joint optimization of the maintenance and rehabilitation policies. The specific transition probabilities of the Markov matrices are estimated for the particular conditions of the Chiapas bridge stock condition state and traffic loading and environmental loads in the area. The optimization problem is solved via a computer application using genetic algorithms (GA) to find the minimum costs for the different maintenance and rehabilitation policies generated by the model. It is shown how the application of the proposed model leads to better budget allocation and less total cost when compared with the standard method of maintenance used till now by the Chiapas State Agency. In bridge engineering, computer simulations have proven to be an important tool for studying some aspects in bridge management. In the last years, several researches have used genetic algorithms (GAs) in optimal models for decisions-making process (Liu et al. 1997, Furuta et al. 2001, Malioka & Onoufriou 2002, Neves 2005). This method implement the idea of goal-oriented design was the application of a search and optimization technique borrowed from the field of artificial intelligence. Gas is based on Darwinian notions of survival of the fittest, where selection and recombination operators are used among candidate solutions to look for the optimal one. They have obtained optimal solutions for their proposed models using this optimization technique. In a bridge, the service life is limited by several factors. Deterioration in different components can be caused by problems during construction or in service stages. Problems such as corrosion, cracking, scours, concrete spalling, vegetation, settlement and others, may be a risk for users and produce a traffic interruption. Applicati
In this paper, a genetic-based algorithm is presented for optimal placement of phasor measurement units, which minimizes the number of the units and enhances the accuracy of the harmonic state estimation solutions. Al...
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This paper discusses the solution of a particular case of grey nonlinear programming, the Grey Quadratic Programming (GQP), and introduces the genetic algorithms (GA) approach as a feasible method for solving GQP prob...
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This paper develops a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced. Some performance indices are expressed...
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This paper develops a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced. Some performance indices are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.
A primary issue in black-box testing is how to generate adequate test cases from input domain of the system under test on the basis of user's requirement specification. However, for some types of systems including...
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Adaptive applications are a key concept to take into account when dealing with multimedia internetworking in wireless and mobile environments in which abrupt changes in the network conditions may occur. In these scena...
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This paper studies the issue of space coordinate change in genetic algorithms, based on two methods: convex quadratic approximations, and principal component analysis. In both methods, the procedure employs only the o...
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ISBN:
(纸本)9781605581309
This paper studies the issue of space coordinate change in genetic algorithms, based on two methods: convex quadratic approximations, and principal component analysis. In both methods, the procedure employs only the objective function samples that have already been obtained through the usual genetic algorithm operations, without the need of any additional function evaluation. The two procedures have been tested over a set of benchmark problems, and the data has been analyzed via a stochastic dominance analysis procedure. In both cases, the results suggest that in the transformed coordinates the genetic algorithm can able to deal with ill-conditioned problems in less iterations and with greater proportion of successful attempts, in comparison to the genetic algorithm without coordinate transformation.
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