Abstract— This paper presents an **algorithm** for **order** **reduction** **of** higher **order** **linear** **interval** system into stable lower **order** **linear** **interval** system by means **of** **Genetic** **algorithm**. In this **algorithm** the numerator and denominator polynomials are determined by minimizing the Integral square error (ISE) **using** **genetic** **algorithm** (GA). The **algorithm** is simple, rugged and computer oriented. It is shown that the **algorithm** has several advantages, e.g. the reduced **order** models retain the steady-state value and stability **of** the original system. A numerical example illustrates the proposed **algorithm**.

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The paper objective is to reveal the importance **of** **genetic** algorithms in building robust metrics **of** collaborative **systems**. The main types **of** collaborative **systems** in economy are presented and some characteristics **of** **genetic** algorithms are described. A **genetic** **algorithm** was implemented in **order** to determine the local maximum and minimum points **of** the relative complexity function associated to a collaborative banking system. The intelligent collaborative **systems** based on **genetic** algorithms, representing the new generation **of** collaborative **systems**, are analyzed and the implementation **of** auto-adaptive interfaces in a banking application is described.

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Abstract—Nonlinear Principal Components Analysis (PCA) addresses the nonlinearity problem by relaxing the **linear** restrictions on standard PCA. A new approach on this subject is proposed in this paper, quasi-**linear** PCA. Basically, it recovers a spline based **algorithm** designed for categorical variables and introduces continuous variables into the framework without the need **of** a discretization process. By **using** low **order** spline transformations the **algorithm** is able to deal with nonlinear relationships between variables and report dimension **reduction** conclusions on the nonlinear transformed data as well as on the original data in a **linear** PCA fashion. The main advantages **of** this approach are; the user do not need to care about the discretization process; the relative distances within each vari- ables’ values are respected from the start without discretization losses **of** information; low **order** spline transformations allow recovering the relative distances and achieving piecewise PCA information on the original variables after optimization. An example applying our approach to real data is provided below.

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In this paper an attempt is made to explore the use **of** real coded **genetic** **algorithm** to find the optimal generation rescheduling for congestion management in deregulated environment. Congestion may occur in power system due to transmission line outages, generator outages, changes in energy demand and uncoordinated transactions. In this work, N-1 contingency analysis is carried out to identify the most severe lines and those lines are considered for analysis. The proposed problem is formulated as an optimization problem having large number **of** constraints. RCGA is used as optimization tool since it is an efficient heuristic **algorithm** for search and optimization. It operates on floating point representation **of** variables to be optimized. The method has been tested on IEEE 30 bus system successfully. In **order** to prove the effectiveness **of** the **algorithm** it is compared with SA. The simulation results proved that the RCGA yield economical solution than SA for all the test cases thereby enhances system security. Further more the line overloads are relieved completely by generation rescheduling alone without any load curtailment.

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To the authors’ best knowledge, the problem **of** “fully coupled vibroacoustic (e.g. acoustic power radiation) optimization **of** finite length submerged ribbed cylinders” has not been addressed in the literature yet. A submerged ribbed cylinder has been considered as a numerical test case (the model described by Zhou & Joseph (2005)) for coupled vibroacoustic FEM/BEM modeling. At first, two procedures have been proposed for computing BEM matrices in each frequency line. In **order** to solve governing equations, dimension **reduction** is inevitable, so the Krylov vectors (produced via Ritz or Arnoldi iterative procedures) and structural mode shapes (modal truncation approach) have been used and compared before performing optimization. The results showed good agreement with previous studies and experiments. Although Krylov **reduction** concept has been developed for simulating interior acoustic problems (cabin noise, etc.), **using** these basis vectors along optimization process **of** exterior vibroacoustic problems has not been reported in the literature. Finally, the optimum concentrated (point) mass and the dynamic absorber (TMD) arrangements were considered for the **reduction** **of** average radiated power **using** **Genetic** **Algorithm**. As expected, the majority **of** total point masses have been proposed by GA for the nearest regions to the excitation point but, this is not true for the case **of** dynamic absorbers (TMD). A noticeable point in the design **of** silent submerged vessels is that the most changes in this case proposed on the opposite side **of** the excitation point.

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Batra and Liang 11 used a three-dimensional **linear** theory **of** elasticity to find the optimal location **of** an actuator on a simple-supported rectangular laminated plate with embedded PZT layers. The optimal design is obtained by fixing the applied voltage and the size **of** the actuator and moving it around in **order** to find the maximum out-**of**-plane displacement. Liang et al. 12 proposed a model for the optimization **of** the induced-strain actuator location and configuration for active vibration control. Correia et al. 13 presented refined finite element models based on higher **order** displacement fields applied to the optimal design **of** laminated composite plates with embedded or surface bonded piezoelectric actuators and sensors.

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Form the testing **of** our **algorithm** which appearing in Table 1 we have a three encrypted images for each original image. Firstly we have obtained a plain image **of** an RGB color. Then this analysis by taking two sequential gray level numbers by constructing 2by2 **linear** system. Finally repeated modular numbers technique is used for production three matrices. Here it is interesting to note that the encrypted image do not has any resemblance with their corresponding original images. This fact ensures security **of** images in an effective manner. The histogram **of** each **of** original image and its decrypted image referee the goodness **of** this **algorithm**. The results **of** these histograms appear in Table 2 where ha and hb represent the histogram **of** original image and decrypted image respectively. The results appearing in Tables 1 and 2 are promised to develop.

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Abstract— In this paper we analyze spatially multiplexed MIMO **systems** with limited Channel State Information (CSI) and zero forcing (ZF) **linear** signal detection technique. Two schemes were considered: Quantization Codebook (QC) and Compressive Sensing (CS). Compressive Sensing is used to generate a reduced CSI feedback to the transmitter in **order** to reduce feedback load into the system. Performance **of** the schemes are compared by computational simulations **of** bit error rate (BER) curves for the considered approaches QC and CS.

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LIU, P.; HARTZELL, S. A; STEPHENSON, W. Nonlinear multiparameter inversion **using** a hybrid global search **algorithm**: applications in reflec- tion seismology. Geophys. J. Int., [S.l.], v. 122, p. 991-1000, 1995. MCGILLIVRAY, P. R.; OLDENBURG, D. W. Methods forcalculating Fréchet derivatives and sensitivities for non-**linear** inverse problem: a compara- tive study. Geophys. Prosp., [S.l.], v. 38, p. 499-524, 1990. MEDEIROS, W. E. Eletro-resistividade aplicada à hidrogeologia do cristalino: um problema de modelamento bidimensional. 1987. Dissertação (Mestrado)-Universidade Federal da Bahia, Salvador, 1987. MEIJERINK, J. A.; VAN DER VORST, H. A. An iterative solution method for **linear** **systems** **of** which the coefficient matrix is a symmetric M-ma- trix. Mathematics **of** Computation, [S.l.], v. 31, p. 148-162, 1977. MENKE, W. Geophysical data analysis: discrete inverse theory. New York: Academic Press, 1989.

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Abstract—This paper discusses the design **of** multiple- input–multiple-output (MIMO) antenna **systems** and proposes a **genetic** **algorithm** to obtain the position and orientation **of** each MIMO array antenna that maximizes the ergodic capacity for a given propagation scenario. One challenging task in the MIMO system design is to accommodate the multiple antennas in the mobile device without compromising the system capacity, due to spatial and electrical constraints. Based on an interface between the antenna model and the propagation channel model, the ergodic capacity is considered as the objective function **of** the MIMO array optimization. Simulation results corroborate the importance **of** polarization and antenna pattern diversities for MIMO in small terminals. Our results also show that the electromagnetic coupling effect can be exploited by the optimizer to decrease signal correlation and increase MIMO capacity. A comparison among a uniform **linear** array (ULA), a uniform circular array (UCA), and a **genetic** **algorithm** (GA)-optimized array is also carried out, showing that the topology given by the optimizer is superior to that **of** the standard ULA and UCA for the considered propagation channel.

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Convex optimization has recently emerged as an effi- cient tool for reducing the PAPR **of** OFDM signals [10-16]. This can be explained in part by the fact that con- vex optimization methods can efficiently compute global solutions to large-scale problems in polynomial time. In [15], an iterative second-**order** cone programming (SOCP) approach was proposed to pursue the quasi-constant PAPR value **of** OFDM signals. In [16], a semidefinite relax- ation (SDR) technique is employed to reduce the PAPR values **of** OFDM symbols. Although convex optimization approaches show advantages over the classical repeated clipping and filtering (RCF) approach [7], it is important to note that they may fail to deliver feasible solution to the PAPR problem; see [15,16]. This is due to the fact that in [15,16], the feasible (nonconvex) set **of** the original (non- convex) PAPR problem lies within the feasible (convex)

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The concept **of** optimization is associated to the determination **of** one or more possible solutions, which represent the extreme values **of** one or more fitness functions. Optimization techniques have a big relevance to the resolution **of** practical problems. When an optimization model represents one real system that involves just one function, it is called a mono objective problem. The problems that we are facing nowadays, in a technologically advanced world are complex. Sometimes the determination **of** optimal solutions may even be impossible and many times what we are looking for is just a good enough approximate solution. The main goal **of** this project is to obtain descriptors for 3D point clouds, **using** a **genetic** **algorithm** to obtain the parameters that will be used to build histograms to represent the point clouds. The descriptors will be tested in a large dataset **of** point clouds in **order** to evaluate their performance and compare them with other already existing descriptors.

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Recently rotational LDA technique [1] is presented which minimizes this limitation **of** LDA technique. In **order** to minimize the overlaps, it utilizes two transforms: rotational transform and orientation W. The rotational transform rotates the original feature space in such a way that thereafter the utilization **of** orientation W produces a reduced feature space which is most discriminative for different classes. The computation **of** is an iterative process which requires some components to be evaluated including the regions in the subspace belonging to classes. The boundaries **of** these regions are computed **using** minimum distance classification method. Therefore the boundaries **of** regions are dependent on the type **of** the classifier used. Thus the choice **of** classifier becomes crucial for separating regions in the reduced feature space which influences the overall classification performance. In this paper we have utilized Bayes decision theorem with Gaussian density function for this purpose. The utilization **of** Bayesian rule seems to improve the performance in terms **of** getting lesser classification error which is empirically demonstrated. Also an adaptive approach is adopted to compute within-class scatter matrix S W for the computation **of** orientation W.

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needed during the synchronization process. By doing so A and B can synchronize their neural networks without transmitting input values over the public channel. **Of** course, an opponent E does not know the secret seed state. Therefore E is unable to synchronize due to the lack **of** information about the input vectors. Even an active man-in-the-middle attack does not help in this situation, although it is always successful for public inputs. Consequently, reaching full synchronization proves that both participants know the secret seed state. Thus A and B can authenticate each other by performing this variant **of** the neural key exchange. The use **of** **genetic** algorithms can fasten the process **of** synchroniza-tion **of** genuine parties, so that many attacks, which are generally successful due to lengthy synchronization process, can be avoided. This makes the brute force attack very difficult. So in this paper we have discussed the Regular flipping Attack (RFA) and Majority Flipping Attack (MFA) strategy [9].

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[2] Saurabh Malpani, Yogesh Yenarkar, Dr. Suhas Deshmukh, S P Tak, D.V. Bhope ―Design **OF** Flexure Bearing For **Linear** Compressor by Optimization Procedure **Using** FEA‖, International Journal **of** Engineering Science and Technology (IJEST) Vol. 4 No.05 May 2012 pp.1991-1999

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Passino (2002) has proposed the Bacterial Foraging **Algorithm** based adaptive controller for a liquid level control problem. The perception **of** foraging activities **of** Escherichia coli (E. coli) bacteria is used for the optimization technique to find out the best fitted PID controller parameters by a set **of** artificial bacteria in the “D” dimensional search space. Many attempts by researchers have been carried out to find the optimal controller parameters **using** Bacterial Foraging **Algorithm** for different categories **of** engineering optimization problems . Datta et al. (2008) have proposed an improved adaptive approach involving Bacterial Foraging **Algorithm** to optimize both the amplitude and phase **of** the weights **of** a **linear** array **of** antennas for maximum array factor at any desired direction and nulls in speciﬁc directions[15]. In their work, it was found that Bacteria Foraging **Algorithm** is capable **of** improving the speed **of** convergence as well as the precision in the desired result. Bhushan et al. (2011) have been implemented the bacterial foraging **algorithm** for identification and high performance speed control system for a DC motor [16]. Recently, Rajinikanth and Latha (2012) discussed about the BFO-tuned I-PD controller performance on a class **of** time delayed unstable process models [17].

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The position **of** beacon in our **algorithm** at middle **of** the frame bottom edge has highlighted the important role **of** this position on the nodes visibility average. So, as compared with the robotic cluster matching **algorithm**, our **algorithm** shows an increase in the visibility average meaning that more accuracy in location estimation will be obtained. Also, our proposed **algorithm** shows a better performance than the robotic cluster matching **algorithm** in addressing the nodes under the effects **of** different parameters such as the rotating angle **of** beacon, nodes radius and the size **of** network.

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Model implementation was applied on real-world collaborative sets **of** data. It works better than comparable models. The present analysis and implementation **of** this approach provides the best results from another used standard methods. The importance **of** **genetic** algorithms **using** is in the following cases. In real-world situations we often have to solve the hard optimization problems that are usually not possible to solve by classical approaches. In practice, we usually have:

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Reasons to choose **linear** Bezier: (1) Due to large size **of** video data simple and efficient fitting is inevitable. **Linear** Bezier is most efficient and simple than other types curves e.g., quadratic/cubic Bezier curves, Natural cubic spline, B-spline, Cardinal spline etc. There is no continuity constraint in the adjacent segments **of** Bezier curves i.e., each **linear** Bezier segment can be constructed independently **of** other segments and abrupt changes in pixel intensity can be approximated by multiple **linear** Bezier segments efficiently. (2) There is no middle control point in **linear** Bezier unlike quadratic/cubic Bezier curves, further end-control points are in the same data range, as original data, i.e., [0-255]. Therefore, **linear** Bezier needs less data (low entropy) to save and is suitable for video data compression. (3) Fitting **of** data by **linear** Bezier is local whereas fitting **of** data **using** other types **of** splines e.g., Natural cubic spline or Cardinal spline is non-local. Local fitting means breaking **of** a segment into two segments in Bezier fitting requires only re-computation **of** two newly created segments without affecting the remaining segments. Therefore whenever a fitted segment splits due to large change in pixel intensity, the computation cost remains within acceptable limit. The locality **of** Bezier fitting is at the expense **of** less continuity. **Linear** Bezier is C 0 continuous, this result in lack **of** smoothness. But this lesser continuity does not impact the quality **of** reconstructed video because it depends on closeness **of** fit to original data rather than smoothness **of** fit. Smoothness **of** fit is desirable feature in applications like font design where smooth curves are more pleasing.

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During the in-the-sample period there are two strong trends: a bullish market until the middle **of** 2008 – despite financial crisis, subprime crisis, has begun in the summer **of** 2007, the bullish trend in foreign exchange market only reversed to a bearish trend in the summer **of** 2008 - and therefore a strong bearish trend took control **of** the market until the end **of** the in-the-sample period, end **of** 2009. Throughout the out-**of**-sample period the market has continued to decrease, however it has found some resistance levels where the market has been moving/moved sideways. In these sideways zones, the market volatility has increased comparing with the trend periods, which creates a good sample to test the best indicator optimized in the out-**of**-sample period, because it is tested in a different environment. It is important to reference that on September 6, 2011, the Suisse National Bank set a minimum exchange rate **of** 1.20 francs per euro, which has decreased its volatility since then.

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