a genetic algorithm approach to the spectral estimation of time series with noise and missed observations this study considers the problem of estimating the autoregressive moving average arma power spectral density when measurements are corrupted by noise and by missed observations the missed observations model is based on a probabilistic structure unlike conventional cases of missed observation in parameter estimation problems the variance of noise is unavailable that is the time points of missed observations are unknown and the probability of missing data needs to be estimated in this situation spectral estimation is more difficult to solve and becomes a highly nonlinear optimization problem with many local minima in this paper we use the genetic algorithm ga method to achieve a global optimal solution with a fast convergence rate for this spectral estimation problem from the simulation results we have determined that the performance is significantly improved if the probability of data loss is considered in the spectral estimation problem

equivariant and fractional index of projective elliptic operators in this note the fractional analytic index for a projective elliptic operator associated to an azumaya bundle of 5 is related to the equivariant index of 1 6 for an associated transversally elliptic operator

iga based point cloud fitting using b spline surfaces for reverse engineering reverse engineering is a viable method to create a 3d virtual model of real physical parts usually reverse engineering consists of two main steps 1 measure the object and 2 reconstruct it as a 3d model the measured data are usually represented as a point cloud without topological information and must therefore often be converted into a tensor product b spline surface format which has become an industry standard in computer graphics and in cad systems in this paper a new immune genetic algorithm iga for point cloud fitting that fits a noisy 3d point cloud using a b spline surface with approximate g1 continuity is presented the point cloud is first segmented into a set of quadrilateral patches for every patch a b spline surface is reconstructed using a least squares approximation method and then the surface is optimized to increase the approximation precision using an iga based knots adjustment algorithm finally the b spline patches are stitched together with approximate g1 continuity with a numerical method and the particle swarm optimization pso algorithm a set of experimental results shows that the proposed method achieves better approximation accuracy than the bezier based method and the ga based method

analytic network process for pattern classification problems using genetic algorithms the analytic network process anp is a useful technique for multi attribute decision analysis mcda that employs a network representation to describe interrelationships between diverse attributes owing to effectiveness of the anp in allowing for complex interrelationships between attributes this paper develops an anp based classifier for pattern classification problems with interdependence or independence among attributes to deal with interdependence this study employs genetic algorithms gas to automatically determine elements in the supermatrix that are not easily user specified to find degrees of importance of respective attributes then with the relative importance for each attribute in the limiting supermatrix the current work determines the class label of a pattern by its synthetic evaluation experimental results obtained by the proposed anp based classifier are comparable to those obtained by other fuzzy or non fuzzy classification methods

gravitation based classification classification is one of the key issues in the fields of decision sciences and knowledge discovery in this paper we present a new classification method based on gravitational potential energy between particles the basic principle of gravitation based classification gbc algorithm is to find the equilibrium condition of the classifier which is modeled as a classifier line between two groups of fixed particles in the proposed approach the input data is a collection of masses which interact with each other based on newton s universal law of gravitation and the laws of motion we present a convex formulation for this problem that always converges to a global optimum solution the proposed method has been compared with some well known classification approaches and the results confirm the high performance of the proposed method

rees algebras and koszul homology abstract this paper deals with properties of a cohen macaulay ideal i in a regular local ring r we consider two main kinds of ideals strongly cohen macaulay ideals and ideals with sliding depth one of our aims is to sharpen their differences we also examine certain kinds of normal ideals and consider ways of determining the divisor class group and the canonical module of its rees algebra we show that the divisor class group of r i is a finitely generated free abelian group the main results of this work are certain numerical constraints that are shown to exist on the projective resolutions of graded ideals with cohen macaulay koszul homology

particle swarm optimization for non uniform rational b spline surface reconstruction from clouds of 3d data points this work investigates the use of particle swarm optimization pso to recover the shape of a surface from clouds of either organized or scattered noisy 3d data points a challenging problem that appears recurrently in a wide range of applications such as cad design data visualization virtual reality medical imaging and movie industries in this paper we apply a pso approach in order to reconstruct a non uniform rational b spline nurbs surface of a certain order from a given set of 3d data points in this case surface reconstruction consists of two main tasks 1 surface parameterization and 2 surface fitting both tasks are critical but also troublesome leading to a high dimensional non linear optimization problem our method allows us to obtain all relevant surface data i e parametric values of data points knot vectors control points and their weights in a shot and no pre post processing is required furthermore it yields very good results even in presence of problematic features such as multi branches high genus or self intersections seven examples including open semiclosed closed zero genus high genus surfaces and real world scanned objects described in free form parametric and implicit forms illustrate the good performance of our approach and its superiority over previous approaches in terms of accuracy and generality

a genetic algorithm approach to determine the sample size for attribute control charts determining the sample size for control charts ccs is generally an important problem in the literature in this paper kaya and engin s i kaya o engin a new approach to define sample size at attributes control chart in multistage processes an application in engine piston manufacturing process journal of materials processing technology 183 2007 38 48 model based on minimum cost and maximum acceptance probability to determine the sample size for attribute control charts accs and solved by genetic algorithms gas with linear binary representation structure is handled to solve it by a linear real valued representation a new chromosome structure is also suggested to increase the efficiency of gas the performance of gas depends on mutation and crossover operators and their ratios to determine the most appropriate operators five different mutation and crossover operators are used and they are compared with each other an application in a motor engine factory is illustrated u control charts are constructed with respect to the sample size determined by ga in the model the piston production stages in this factory are monitorized using the obtained control charts

f injectivity and buchsbaum singularities let r mathfrak m k be a local ring that contains a field we show that when r has equal characteristic p 0 and when h mathfrak m i r has finite length for all i dim r then r is f injective if and only if every ideal generated by a system of parameters is frobenius closed as a corollary we show that such an r is in fact a buchsbaum ring this answers positively a question of s takagi that f injective singularities with isolated non cohen macaulay locus are buchsbaum we also study the characteristic 0 analogue of this question and we show that du bois singularities with isolated non cohen macaulay locus are buchsbaum in the graded case

on the finite generation of symbolic blow ups symbolic blow up p n s a noetherian ring if s is noetherian and dim n 0 rip l cowsik observed that p must be a set theoretic complete intersection if r is not regular there is no hope in general of s being noetherian in fact if r is any normal two dimensional local ring and p is a height one prime ideal of r which is not torsion in the class group of r then s is never noetherian this idea is behind the rees counterexample 6 to the generalized zariski question as far as the author knows there are no known counter examples to the question of cowsik the most trivial case is the following if r is nagata and

castelnuovo mumford regularity and ratliff rush closure abstract we establish strong relationships between the castelnuovo mumford regularity and the ratliff rush closure of an ideal our results have several interesting consequences on the computation of the ratliff rush closure the stability of the ratliff rush filtration the invariance of the reduction number and the computation of the castelnuovo mumford regularity of the rees algebra and the fiber ring in particular we prove that the castelnuovo mumford regularity of the rees algebra and of the fiber ring are equal for large classes of monomial ideals in two variables thereby verifying a conjecture of eisenbud and ulrich for these cases

the largest non vanishing degree of graded local cohomology modules abstract this paper gives characterizations for the largest non vanishing degree of the local cohomology modules of a graded ring s in terms of a reduction of s and of the associated graded ring of an ideal i in terms of any reduction of i as a consequence this invariant can be computed explicitly for the associated graded ring of any ideal generated by a d sequence and of ideals having small analytic deviation

path planning on a cuboid using genetic algorithms the traveling salesman problem tsp is one of the most extensively studied problems in the fields of combinatorial optimization and global search heuristics a variety of heuristic algorithms are available for solving euclidean tsp and planar tsps however optimization on a cuboid has potential applications for areas like path planning on the faces of buildings rooms furniture books and products or simulating the behaviors of insects in this paper we address a variant of the tsp in which all points cities and paths solution are on the faces of a cuboid we develop an effective hybrid method based on genetic algorithms and 2 opt to adapt the euclidean tsp to the surface of a cuboid the method was tested on some benchmark problems from tsplib with satisfactory results a web based interactive visualization tool has also been developed using java 3d and optimization results for different point densities on the cuboid are presented

gp coach genetic programming based learning of compact and accurate fuzzy rule based classification systems for high dimensional problems in this paper we propose gp coach a genetic programming based method for the learning of compact and accurate fuzzy rule based classification systems for high dimensional problems gp coach learns disjunctive normal form rules generated by means of a context free grammar coded as one rule per tree the population constitutes the rule base so it is a genetic cooperative competitive learning approach gp coach uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules the results obtained have been validated by the use of non parametric statistical tests showing a good performance in terms of accuracy and interpretability

on three dimensional surface reconstruction methods a survey is presented of some of the surface reconstruction methods that can be found in the literature the focus is on a small recent and important subset of the published reconstruction techniques the techniques are classified based on the surface representation used implicit versus explicit functions a study is made of the important aspects of the surface reconstruction techniques one aspect is the viewpoint invariance of the methods this is an important property if object recognition is the ultimate objective the robustness of the various methods is examined it is determined whether the parameter estimates are biased and the sensitivity to obscuration is addressed the latter two aspects are particularly important for fitting functions in the implicit form a detailed description is given of a parametric reconstruction method for three dimensional object surfaces that involves numeric grid generation techniques and variational principle formulations this technique is invariant to rigid motion in dimensional space

the theory of d sequences and powers of ideals in this paper we study the question of finding depth r i given an ideal i in a commutative ring this problem is difficult in general even for simple examples depth r z can be difficult to compute brodmann has shown that for any commutative noetherian ring depth r z become stable for large n finding this value however is quite hard this problem originally arose through consideration of the following example suppose k is a field and x xij is an n

self organizing genetic algorithm based tuning of pid controllers this paper proposes a self organizing genetic algorithm soga with good global search properties and a high convergence speed first we introduce a new dominant selection operator that enhances the action of the dominant individuals along with a cyclical mutation operator that periodically varies the mutation probability in accordance with evolution generation found in biological evolutionary processes next the soga is constructed using the two operators mentioned above the results of a nonlinear regression analysis demonstrate that the self organizing genetic algorithm is able to avoid premature convergence with a higher convergence speed and also indicate that it possesses self organization properties finally the new algorithm is used to optimize proportional integral derivative pid controller parameters our simulation results indicate that a suitable set of pid parameters can be calculated by the proposed soga

a note on complete intersections let r be a regular local ring and let r t be a polynomial algebra in one variable over r in this paper the author proves that every maximal ideal of r t is complete intersection in each of the following cases 1 r is a local ring of an affine algebra over an infinite perfect field 2 r is a power series ring over a field introduction let f be a regular local ring let r t be a polynomial algebra in one variable over r in d g the following question has been asked question is every maximal ideal of r t complete intersection in this paper we prove that the answer to the above question is affirmative in each of the following cases 1 f is a local ring of an affine algebra over an infinite perfect field 2 f is a power series ring over a field this paper is divided into three sections in 1 we fix notations and state a theorem without proof which is used in 2 and 3 in 2 we prove some lemmas and propositions which are used in proving the result when f is a local ring of an affine algebra 3 deals with the power series case 1 throughout this paper we consider commutative noetherian rings with 1 for a ring r dim r denotes its krull dimension which we always assume to be finite if r is a local ring then 911 f will always denote its unique maximal ideal if m is a finitely generated f module then ii m will denote the minimal number of genera tors of m for an ideal i of r i i denotes the height of definition let be an unmixed ideal of r of height r then i is said to be complete intersection in r if i 2lri xrai where ax a2 ar is a regular r sequence remark if r is cohen macaulay then i is complete intersection if and only if p ht 7 let r and s be two local rings definition r is said to be a local extension of s if s is a subring of r and 91t s 911 f d s r is said to be unramified over s if gjt s r 911 f and f 91t f is separable over 5 91l s let l k be a finite separable extension of k then l is a simple extension of k by a minimal polynomial of l over k we always mean an irreducible monic polynomial over k satisfied by a generator of l over k now we state a theorem which has been proved in d g theorem 3

normal sally modules of rank one in this paper we explore the structure of the normal sally modules of rank one with respect to an m m primary ideal in a nagata reduced local ring r which is not necessary cohen macaulay as an application of this result when the base ring is cohen macaulay analytically unramified the extremal bound on the first normal hilbert coefficient leads to the depth of the associated graded rings g with respect to a normal filtration is at least dim r 1 dim r 1 and g turns in to cohen macaulay when the third normal hilbert coefficient is vanished

on rings with linear resolutions let r denote a positively graded noetherian ring over a field k r eori with r k and m the irrelevant maximal ideal generated by forms of degree 1 or let r be the m adic completion of such a ring if we consider x as a homomorphic image of a polynomial ring resp of a power series ring over k with a homogeneous defining ideal one may ask whether r has a minimal linear resolution as an s module cohen macaulay rings r of maximal embedding dimension e g have a linear resolution as sally showed in 9 she also proved formulas for the hilbert series hr and the poincari series pi pz schenzel lo later characterized cm rings r with a linear resolution and called these rings extremal he also gave formulas for hr and pi s goto see 3 1 below proved besides other facts the linearity of the resolution for buchsbaum rings of maximal embedding dimension i e edim r dim r e r i r 1 i r denotes the invariant of the buchsbaum ring r and a formula for hh in section 1 we generalize these results and characterize rings with a linear resolution which have a parameter system that forms a d sequence in the buchsbaum case we then show that the h pi and pf of such rings are determined by the embedding dimension the krull dimension the dimensions of the local cohomology modules as k vector spaces and what we call the type of r in section 2 we determine all buchsbaum rings r k r of maximal embedding dimension and multiplicity 2 sally proved in 8 that for a local cm ring r m of maximal embedding dimension the associated graded ring is also cm of maximal embedding dimension goto showed a similar result for buchsbaum rings of

associating visual textures with human perceptions using genetic algorithms this paper deals with an approach allowing to associate visual textures with given human perceptions hereby based on a forward model associating human perceptions for given visual textures the deduction of an reverse process is presented which is able to associate and characterize visual textures for given human perceptions for doing so we propose a constraint based genetic algorithm approach which is able to minimize a specific optimization problem containing constraints in form of band widths for valid individuals low level features extracted from textures in a population the constraints are determined by relationships between low level features characterizing textures in form of high dimensional approximation models additionally in each iteration step checking for valid individuals is carried out with a texture non texture classifier or by using a convex hull over a set of valid textures the whole approach is evaluated based on a real world texture set used as a start population in the genetic algorithm and by defining various kinds of human perceptions for which textures are sought represented by adjective vectors in the aesthetic space the generated individuals low level feature vectors have a high level of fitness they are quite close to the pre defined adjective vectors and a small distance to the initial population the textures synthesized based on the generated individuals are visualized and compared with textures synthesized by a time intensive direct texture mixing and re combination method based on a real world texture data base

filtrations and local syzygies of multiplier ideals abstract recently robert lazarsfeld and kyungyong lee proved an interesting result on local syzygies of multiplier ideals in this note we continue their investigation by showing an asymptotic behavior of local syzygies of multiplier ideals in filtrations induced by maximal ideal sheaves

iterative two step genetic algorithm based method for efficient polynomial b spline surface reconstruction surface reconstruction is a very challenging problem arising in a wide variety of applications such as cad design data visualization virtual reality medical imaging computer animation reverse engineering and so on given partial information about an unknown surface its goal is to construct to the extent possible a compact representation of the surface model in most cases available information about the surface consists of a dense set of either organized or scattered 3d data points obtained by using scanner devices a today s prevalent technology in many reverse engineering applications in such a case surface reconstruction consists of two main stages 1 surface parameterization and 2 surface fitting both tasks are critical in order to recover surface geometry and topology and to obtain a proper fitting to data points they are also pretty troublesome leading to a high dimensional nonlinear optimization problem in this context present paper introduces a new method for surface reconstruction from clouds of noisy 3d data points our method applies the genetic algorithm paradigm iteratively to fit a given cloud of data points by using strictly polynomial b spline surfaces genetic algorithms are applied in two steps the first one determines the parametric values of data points the later computes surface knot vectors then the fitting surface is calculated by least squares through either svd singular value decomposition or lu methods the method yields very accurate results even for surfaces with singularities concavities complicated shapes or nonzero genus six examples including open semi closed and closed surfaces with singular points illustrate the good performance of our approach our experiments show that our proposal outperforms all previous approaches in terms of accuracy and flexibility

type 1 and type 2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms we describe in this paper a comparative study between fuzzy inference systems as methods of integration in modular neural networks for multimodal biometry these methods of integration are based on techniques of type 1 fuzzy logic and type 2 fuzzy logic also the fuzzy systems are optimized with simple genetic algorithms with the goal of having optimized versions of both types of fuzzy systems first we considered the use of type 1 fuzzy logic and later the approach with type 2 fuzzy logic the fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions like the triangular trapezoidal and gaussian since these algorithms can generate fuzzy systems automatically then the response integration of the modular neural network was tested with the optimized fuzzy systems of integration the comparative study of the type 1 and type 2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry

a hybrid genetic algorithm with the baldwin effect here we present a new hybrid genetic algorithm hga with the baldwin effect in the hga a local search is employed to change the fitness of individuals but the acquired improvements do not change the individual itself this local search step exploits the baldwin effect some numerical applications show that this algorithm can yield the global optimum more efficiently than commonly used hgas a theorem is presented that guarantees the convergence in probability of the new hga

an intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms time series observed in real world is often nonlinear even chaotic however observed data is often contaminated by noise of various types to effectively extract desired information from observed data it is vital to preprocess data to reduce noise for both the analysis of dynamical systems and many potential applications of these systems in this paper we present a noise reduction approach to the problem of additive source separation characterized by wide band power spectra when one of the sources is chaotic the algorithm is based on a center based genetic algorithm cbga in lifting wavelet framework in which the cbga is used for threshold optimization this method intelligently adapts itself to various types of noise and it weighs preservation of dynamics and denoising through signal to noise ratio snr and root mean square error rmse computer simulations show that the approach is very effective in diminishing different kinds of noise and performs better in terms of visual quality as well as quantitative metrics than existing algorithms

approximation complexes of blowing up rings ii this paper is a sequel to 111 where we studied the relationships that hold between the arithmetical properties of the rees ring 9 i and the symmetric algebra sym l of an ideal i and some of their fibers and the depth properties of the koszul homology modules h i r on a set of generators of i the connection between these objects is realized by certain differential graded algebras the so called approximation complexes that are built out of ordinary koszul complexes these compiexes show however different sensitivities being acyclic in situations much broader than the usual context of regular sequences it has been found that certain extensions thereof d sequences and proper sequences play here that role of acyclic sequence the interplay between sequences and approximation complexes will be a basic point of view here where we give

fuzzy spline interpolation with optimal property in parametric form in this paper we propose a new fuzzy interpolation procedure that uses cubic splines with an optimal property obtained in parametric form this fuzzy interpolation operator is also a linear operator of uniform approximation that preserves the monotonicity moreover by integrating this fuzzy cubic spline we obtain the trapezoidal quadrature rule for fuzzy henstock integrable functions

macaulayfication of noetherian schemes to reduce to resolving cohen macaulay singularities faltings initiated the program of macaulayfying a given noetherian scheme x for a wide class of x kawasaki built the sought for cohen macaulay modifications with a crucial drawback that his blowups did not preserve the locus cm x x where x is already cohen macaulay we extend kawasaki s methods to show that every quasi excellent noetherian scheme x has a cohen macaulay x with a proper map x x that is an isomorphism over cm x this completes faltings s program reduces the conjectural resolution of singularities to the cohen macaulay case and implies that every proper smooth scheme over a number field has a proper flat cohen macaulay model over the ring of integers

particle based meta model for continuous breakpoint optimization in smooth local support curve fitting we compute a smooth meta model of a given set of data points based on local support free form parametric curves our method applies a particle based metaheuristic approach to determine optimal values of unknowns of the fitting curve the method does not assume any knowledge about the underlying function of data beyond the data points our approach performs well and in a fully automatic way even for underlying functions exhibiting challenging features our experiments show that our approach outperforms previous approaches in terms of generality and fitting error accuracy this paper concerns the process of computing the underlying function of a given set of data points in many cases it is not possible to obtain an analytical solution for this problem so the goal is transformed into that of computing a meta model instead in this paper we seek to compute a smooth meta model of such points based on local support free form parametric curves given an initial parameterization our method applies a particle based metaheuristic approach to determine optimal values for the breakpoints and poles of the fitting curve which is well known to be a continuous nonlinear optimization problem the performance of our approach is evaluated by its application to two illustrative examples a synthetic academic shape and a real world shape our experimental results show that the proposed scheme performs very well even for shapes with underlying functions exhibiting challenging features such as self intersections and sharp changes of curvature comparative results show that our approach outperforms previous approaches in terms of generality and fitting error accuracy

normal cones of monomial primes we explicitly calculate the normal cones of all monomial primes which define the curves of the form tl tl 1 tl n where n 4 all of these normal cones are reduced and cohen macaulay and their reduction numbers are independent of the reduction these monomial primes are new examples of integrally closed ideals for which the product with the maximal homogeneous ideal is also integrally closed substantial use was made of the computer algebra packages maple and macaulay2