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According to the rough set theory, this paper introduced a neuro-rough model and extends this to a probabilistic domain using a Bayesian framework, trained using a Markov Chain Monte Carlo simulation and the Metropolis algorithms. Firstly, rough set theory was presented, including the granulation of rough set membership function, the network weight formula of probability and rough set formulation...
A robust genetic circuit optimizer using Unscented Transform and Non-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant computational cost reduction compared to Monte Carlo method. The Unscented Transform permits circuit performance uncertainties determination from components uncertainties, thus, a search through robustness can be approached. Besides reduced computational...
The aim of paper is to use Genetic algorithm and Monte Carlo to price convertible bond with finite maturity. As we known, Monte Carlo is hardly applied to price the derivatives with optimal items. Combined with Genetic algorithm and Cubic sample function, Monte Carlo not only solves the convertible bond with optimal conversion items, but also prices that with long-range dependence property. By evaluating...
We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to a multi-objective fleet-mix problem for risk mitigation. The Stochastic Fleet Estimation (SaFE) model, a Monte Carlo-based model, is used to determine average annual requirements which a fleet must meet. We search for Pareto-optimal combinations of platform-to-task assignments that can be used to complete SaFE generated scenarios...
We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the stochastic fleet estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. We search for Pareto-optimal combinations of valid platform-assignments for a list of tasks, which can...
We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the stochastic fleet estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. A genetic algorithm framework is used in order to alternate solutions between different plausible sets...
A novel technique is proposed in this paper that achieves a yield optimized design from a set of optimal performance points on the Pareto front. Trade-offs among performance functions are explored through multi-objective optimization and Monte Carlo simulation is used to find the design point producing the best overall yield. One advantage of the approach presented is a reduction in the computational...
Metropolis sampling is the earliest Markov chain Monte Carlo (MCMC) method and MCMC has been widely used in motif-finding via sequence local alignment. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behavior. To overcome these difficulties, it is common either to run a population of chains or incorporate the evolutionary computing techniques into the...
With the increasing business in air travel area, reducing the plane's turnaround time is becoming more and more important. In this paper, it chooses the optimum boarding strategy to reduce the turnaround time. The MINPL model is for the small-size plane, with the boarding time mainly depending on seat interference and aisle interference. The GASimplex algorithm (genetic algorithms mixed with simplex...
In this paper, we propose a novel algorithm using the NAMK (Non-symmetry and Anti-packing pattern representation Model with K-lines) for the binary image representation. By comparing the algorithm using the NAMK with that using the popular linear quadtree, the theoretical and experimental results presented in this paper show that the former can reduce the data storage much more effectively than the...
This article is an extension of the work presented earlier, which compared and analyzed the economics of alternative maintenance plans. The proposed model combines genetic algorithms with Monte Carlo simulation to arrive at the most economic investment timing. The approach described earlier was characterized by a very long computing time making it difficult to use. This paper addresses several issues...
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