a graph based hyper heuristic for educational timetabling problems this paper presents an investigation of a simple generic hyper heuristic approach upon a set of widely used constructive heuristics graph coloring heuristics in timetabling within the hyperheuristic framework a tabu search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems this underpins a multi stage hyper heuristic where the tabu search employs permutations upon a different number of graph heuristics in two stages we study this graph based hyper heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper heuristic the heuristic space and the solution space such issues have not been addressed in other hyper heuristic research these approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine tuned bespoke state of the art approaches the results are within the range of the best results reported in the literature the approach described here represents a significantly more generally applicable approach than the current state of the art in the literature future work will extend this hyper heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems

weighted centroid neural network for edge preserving image compression an edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed the proposed neural network the called weighted centroid neural network wcnn utilizes the characteristics of image blocks from edge areas the mean residual vector quantization m rvq scheme is utilized in this proposed approach as the framework of the proposed algorithm the edge strength of image block data is utilized as a tool to allocate the proper code vectors in the proposed wcnn the wcnn successfully allocates more code vectors to the image block data from edge area while it allocates less code vectors to the image black data from shade or non edge area when compared to conventional neural networks based on vq algorithm as a result a simple application of wcnn to an image compression problem gives improved edge characteristics in reconstructed images over conventional neural network based on vq algorithms such as self organizing map som and adaptive som

a new preprocessing algorithm for the computation of the generalized singular value decomposition in this note a new algorithm is proposed for the preprocessing phase of paige s algorithm for computing the generalized singular value decomposition gsvd this new algorithm substantially reduces the complexity of paige s algorithm and makes it much easier to implement it is also proved that the preprocessing phase is backward stable and a numerical example is demonstrated

application of partition based median type filters for suppressing noise in images an adaptive median based filter is proposed for removing noise from images specifically the observed sample vector at each pixel location is classified into one of m mutually exclusive partitions each of which has a particular filtering operation the observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of cwm center weighted median filters with variable center weights the estimate at each location is formed as a linear combination of the outputs of those cwm filters and the current pixel value to control the dynamic range of filter outputs a location invariance constraint is imposed upon each weighting vector the weights are optimized using the constrained lms least mean square algorithm recursive implementation of the new filter is then addressed the new technique consistently outperforms other median based filters in suppressing both random valued and fixed valued impulses and it also works satisfactorily in reducing gaussian noise as well as mixed gaussian and impulse noise

computation of system balancing transformations and other applications of simultaneous diagonalization algorithms an algorithm is presented in this paper for computing state space balancing transformations directly from a state space realization the algorithm requires no squaring up or unnecessary matrix products various algorithmic aspects are discussed in detail a key feature of the algorithm is the determination of a contragredient transformation through computing the singular value decomposition of a certain product of matrices without explicitly forming the product other contragredient transformation applications are also described it is further shown that a similar approach may be taken involving the generalized singular value decomposition to the classical simultaneous diagonalization problem these svd based simultaneous diagonalization algorithms provide a computational alternative to existing methods for solving certain classes of symmetric positive definite generalized eigenvalue problems

lower bounds for the itc 2007 curriculum based course timetabling problem abstract this paper describes an approach for generating lower bounds for the curriculum based course timetabling problem which was presented at the international timetabling competition itc 2007 track 3 so far several methods based on integer linear programming have been proposed for computing lower bounds of this minimization problem we present a new partition based approach that is based on the divide and conquer principle the proposed approach uses iterative tabu search to partition the initial problem into sub problems which are solved with an ilp solver computational outcomes show that this approach is able to improve on the current best lower bounds for 12 out of the 21 benchmark instances and to prove optimality for 6 of them these new lower bounds are useful to estimate the quality of the upper bounds obtained with various heuristic approaches

on the use of multi neighbourhood structures within a tabu based memetic approach to university timetabling problems finding a good university timetabling system is not a simple task for a higher educational organisation as a result many approaches to generating sufficiently good solutions have been introduced this is mainly due to the high complexity within the search landscape moreover each educational organisation has its own rules and specifications in this paper a tabu based memetic algorithm that hybridises a genetic algorithm with a tabu search algorithm is proposed as an improved algorithm for university timetabling problems this algorithm is employed on a set of neighbourhood structures during the search process with the aim of gaining significant improvements in solution quality the sequence of neighbourhood structures has been considered to understand its effect on the search space random best and general sequences of neighbourhood structures have been evaluated in this work the concept of a tabu list is embedded to control the selection of neighbourhood structures that are not dependent on the problem domains during the optimisation process after the crossover and mutation operators are applied to the selected solutions from the population pool the algorithm will penalise neighbourhood structures that are unable to generate better solutions the proposed algorithm has been applied and evaluated against the latest methodologies in the literature with respect to standard benchmark problems we demonstrate that the proposed algorithm produces some of the best known results when tested on itc2007 competition datasets

a simple neuro fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images a new impulse noise detector based on neuro fuzzy methods is presented the proposed detector comprises two identical neuro fuzzy subdetectors combined with a decision maker the internal parameters of the subdetectors are adaptively adjusted by training training of the subdetectors is accomplished by using a simple computer generated artificial image the detector can be combined with any impulse noise removal operator the operation of the detector is completely independent of the noise removal operator and it has no influence on the filtering behavior of the operator experimental results show that the proposed detector significantly reduces the distortion effects of any impulse noise removal operator even if the operator already has its own noise detector

iterated tabu search and variable neighborhood descent for packing unequal circles into a circular container this paper presents an iterated tabu search and variable neighborhood descent its vnd algorithm for packing unequal circles into a circular container pucc the algorithm adapts the tabu search procedure of iterated tabu search algorithms and proposes a tabu search and variable neighborhood descent ts vnd procedure we observe there are strong complementarities between the small circles and the large vacant places and propose the insert neighborhood to match up the small circles with the large vacant places although the insert neighborhood is inefficient and time consuming it is an important supplement to the classic swap neighborhood as it could arrange the small circles properly predicated on these features we employ the insert neighborhood only at chosen local minima of the swap neighborhood that shows promise for an improvement the traditional tabu search procedure is then transformed into a hybrid procedure composed of two alternative parts namely variable neighborhood descent and tabu search respectively besides this reformed procedure its vnd also incorporates other new features such as an adaptive evaluation function a novel method for accelerating the neighborhood exploration and the collision accidents criterion for evaluating how intensively the area near the current solution has been explored the computational results on three well established benchmark sets show that the proposed algorithm not only has a good discovery capability but also can provide good results within a reasonable time for a total of 84 benchmark instances the proposed algorithm improves the best known results on 23 instances matches 60 and only misses one

adaptive tabu search for course timetabling this paper presents an adaptive tabu search algorithm denoted by ats for solving a problem of curriculum based course timetabling the proposed algorithm follows a general framework composed of three phases initialization intensification and diversification the initialization phase constructs a feasible initial timetable using a fast greedy heuristic then an adaptively combined intensification and diversification phase is used to reduce the number of soft constraint violations while maintaining the satisfaction of hard constraints the proposed ats algorithm integrates several distinguished features such as an original double kempe chains neighborhood structure a penalty guided perturbation operator and an adaptive search mechanism computational results show the high effectiveness of the proposed ats algorithm compared with five reference algorithms as well as the current best known results this paper also shows an analysis to explain which are the essential ingredients of the ats algorithm

adaptive neighborhood search for nurse rostering abstract this paper presents an adaptive neighborhood search method ans for solving the nurse rostering problem proposed for the first international nurse rostering competition inrc 2010 ans uses jointly two distinct neighborhood moves and adaptively switches among three intensification and diversification search strategies according to the search history computational results assessed on the three sets of 60 competition instances show that ans improves the best known results for 12 instances while matching the best bounds for 39 other instances an analysis of some key elements of ans sheds light on the understanding of the behavior of the proposed algorithm

analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem this paper provides a mathematical treatment of the np hard post enrolment based course timetabling problem and presents a powerful two stage metaheuristic based algorithm to approximately solve it we focus particularly on the issue of solution space connectivity and demonstrate that when this is increased via specialised neighbourhood operators the quality of solutions achieved is generally enhanced across a well known suite of benchmark problem instances our proposed algorithm is shown to produce results that are superior to all other methods appearing in the literature however we also make note of those instances where our algorithm struggles in comparison to others and offer evidence as to why

rank conditioned rank selection filters for signal restoration a class of nonlinear filters called rank conditioned rank selection rcrs filters is developed and analyzed in this paper the rcrs filters are developed within the general framework of rank selection rs filters which are filters constrained to output an order statistic from the observation set many previously proposed rank order based filters can be formulated as rs filters the only difference between such filters is in the information used in deciding which order statistic to output the information used by rcrs filters is the ranks of selected input samples hence the name rank conditioned rank selection filters the number of input sample ranks used is referred to as the order of the rcrs filter the order can range from zero to the number of samples in the observation window giving the filters valuable flexibility low order filters can give good performance and are relatively simple to optimize and implement if improved performance is demanded the order can be increased but at the expense of filter simplicity in this paper many statistical and deterministic properties of the rcrs filters are presented a procedure for optimizing over the class of rcrs filters is also presented finally extensive computer simulation results that illustrate the performance of rcrs filters in comparison with other techniques in image restoration applications are presented

iterated tabu search for the circular open dimension problem abstract this paper investigates the circular open dimension problem codp which consists of packing a set of circles of known radii into a strip of fixed width and unlimited length without overlapping the objective is to minimize the length of the strip in this paper codp is solved by a series of sub problems each corresponding to a fixed strip length for each sub problem an iterated tabu search approach named its is proposed its starts from a randomly generated solution and attempts to gain improvements by a tabu search procedure after that if the obtained solution is not feasible a perturbation operator is subsequently employed to reconstruct the incumbent solution and an acceptance criterion is implemented to determine whether or not accept the perturbed solution as a supplementary method the length of the strip is determined in monotonously decreasing way with the aid of some post processing techniques the search terminates and returns the best found solution after the allowed computation time has been elapsed computational experiments based on numerous well known benchmark instances show that its produces quite competitive results with respect to the best known results while the computational time remains reasonable for each instance

efficient impulse noise reduction via local directional gradients and fuzzy logic this paper proposes an efficient noise reduction method for image restoration a novel pixel classification method by using fuzzy logic and local gradients is first introduced to discriminate noise and noise free pixels from corrupted images then a switching filtering scheme is employed to noisy images the noise pixels will be filtered by a weighted median filter with adaptive weights when the noise free pixels are preserved in the filtered images both numerical comparisons and perceptual observations are demonstrated in the experimental simulations it is found that the proposed method achieves a fairly better performance than a number of other existing methods in noise reduction and image restoration

noise adaptive soft switching median filter existing state of the art switching based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference this reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter we propose a novel switching based median filter with incorporation of fuzzy set concept called the noise adaptive soft switching median nasm filter to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations the proposed nasm filter consists of two stages a soft switching noise detection scheme is developed to classify each pixel to be uncorrupted pixel isolated impulse noise nonisolated impulse noise or image object s edge pixel no filtering or identity filter standard median sm filter or our developed fuzzy weighted median fwm filter will then be employed according to the respective characteristic type identified experimental results show that our nasm filter impressively outperforms other techniques by achieving fairly close performance to that of ideal switching median filter across a wide range of noise densities ranging from 10 to 70

a hybrid neuro fuzzy filter for edge preserving restoration of images corrupted by impulse noise a new operator for restoring digital images corrupted by impulse noise is presented the proposed operator is a hybrid filter obtained by appropriately combining a median filter an edge detector and a neuro fuzzy network the internal parameters of the neuro fuzzy network are adaptively optimized by training the training is easily accomplished by using simple artificial images that can be generated in a computer the most distinctive feature of the proposed operator over most other operators is that it offers excellent line edge detail and texture preservation performance while at the same time effectively removing noise from the input image extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image

a new efficient approach for the removal of impulse noise from highly corrupted images a new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank ordered pixels in a sliding window as part of this framework several algorithms are examined each of which is applicable to fixed and random valued impulse noise models first a simple two state approach is described in which the algorithm switches between the output of an identity filter and a rank ordered mean rom filter the technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter for a small additional cost in memory this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and rom filter in which the weighting coefficients can be optimized using image training data extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40 impulse noise corruption moreover the method can effectively restore images corrupted with gaussian noise and mixed gaussian and impulse noise finally the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise

tri state median filter for image denoising a novel nonlinear filter called tri state median tsm filter is proposed for preserving image details while effectively suppressing impulse noise we incorporate the standard median sm filter and the center weighted median cwm filter into a noise detection framework to determine whether a pixel is corrupted before applying filtering unconditionally extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation

on the nonuniqueness of the factorization factors in the product singular value decomposition abstract the product singular value decomposition psvd of two matrices is revisited in this paper the nonuniqueness of the factorization factors in the psvd is characterized in a way different from that in existing work

mathematical programming models and algorithms for a class faculty assignment problem this paper presents mathematical programming models for assigning faculty members to classes including among typical academic class scheduling issues certain specialized central policies at kuwait university the time slots for classes are initially assumed to be given and an integer programming model cfam is constructed to solve the resulting problem which aims to minimize the individual and collective dissatisfaction of faculty members in a fair fashion where dissatisfaction is measured by a function of the assignment of faculty members to time slots and specific classes in order to enhance the quality of results obtained in practice the model is modified ecfam so that the time slots for the classes can be changed however with restrictions related to efficient facility utilization and permitting an administratively regulated maximum number of changes gender based modeling considerations are also introduced in order to maintain desirable class offering patterns computational results are presented based on solving the models directly by the cplex mip version 7 5 package and also using a specialized lp based heuristic the faculty schedules generated via the proposed approach based on a number of case studies related to the department of mathematics and computer science at kuwait university reveal that this approach yields improved schedules in terms of fairness and enhanced satisfaction levels among faculty members

an adaptive neuro fuzzy system for automatic image segmentation and edge detection an autoadaptive neuro fuzzy segmentation and edge detection architecture is presented the system consists of a multilayer perceptron mlp like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre selected by a fuzzy clustering technique the proposed architecture is feedforward but unlike the conventional mlp the learning is unsupervised the output status of the network is described as a fuzzy set fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels the proposed system is capable to perform automatic multilevel segmentation of images based solely on information contained by the image itself no a priori assumptions whatsoever are made about the image type features contents stochastic model etc such an universal algorithm is most useful for applications that are supposed to work with different and possibly initially unknown types of images the proposed system can be readily employed as is or as a basic building block by a more sophisticated and or application specific image segmentation algorithm by monitoring the fuzzy entropy relaxation process the system is able to detect edge pixels

a methodology for determining an effective subset of heuristics in selection hyper heuristics we address the important step of determining an effective subset of heuristics in selection hyper heuristics little attention has been devoted to this in the literature and the decision is left at the discretion of the investigator the performance of a hyper heuristic depends on the quality and size of the heuristic pool using more than one heuristic is generally advantageous however an unnecessary large pool can decrease the performance of adaptive approaches our goal is to bring methodological rigour to this step the proposed methodology uses non parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances in order to produce a compact subset of effective heuristics for the underlying problem we also propose a new iterated local search hyper heuristic using multi armed bandits coupled with a change detection mechanism the methodology is tested on two real world optimization problems course timetabling and vehicle routing the proposed hyper heuristic with a compact heuristic pool outperforms state of the art hyper heuristics and competes with problem specific methods in course timetabling even producing new best known solutions in 5 out of the 24 studied instances

design of weighted order statistic filters using the perceptron algorithm we observe that the design of optimal weighted order statistic wos filters under the mean absolute error criterion can be thought of as a two class linear classification problem based on this observation the perceptron algorithm is applied to designing wos filters it is shown experimentally that the perceptron algorithm can find optimal or near optimal wos filters in practical situations

switching based filter based on dempster s combination rule for image processing in this work a two pass switching rank ordered arithmetic mean tsram filter that preserves image details while effectively suppressing impulse noise is proposed to improve the performance of switching based median filters the proposed filter mechanism includes an efficient impulse detector based on the dempster shafer d s evidence theory and a rank ordered arithmetic mean filter that works by estimating the noise free ordered mean values excluding the current pixel a modified dempster s combination rule is applied to the impulse detector to improve filtering performance a simple switching median filter is included to perform second pass filtering experimental results show that the proposed filter outperforms many well accepted switching based median filters in terms of both noise suppression and detail preservation providing excellent robustness at various percentages of impulse noise

stack filters and the mean absolute error criterion a method to determine the stack filter which minimizes the mean absolute error between its output and a desired signal given noisy observations of this desired signal is presented specifically an optimal window width b stack filter can be determined with a linear program with o b2 sup b variables this algorithm is efficient since the number of different inputs to a window width b filter is m sup b if the filter has m valued input and the number of stack filters grows faster than 2 raised to the 2 sup b 2 power it is shown that optimal stack filtering under the mean absolute error criterion is analogous to optimal linear filtering under the mean squared error criterion both linear filters and stack filters are defined by superposition properties both classes are implementable and both have tractable procedures for finding the optimal filter under an appropriate error criterion

analysis of the properties of median and weighted median filters using threshold logic and stack filter representation the deterministic properties of weighted median wm filters are analyzed threshold decomposition and the stacking property together establish a unique relationship between integer and binary domain filtering the authors present a method to find the weighted median filter which is equivalent to a stack filter defined by a positive boolean function because the cascade of wm filters can always be expressed as a single stack filter this allows expression of the cascade of wm filters as a single wm filter a direct application is the computation of the output distribution of a cascade of wm filters the same method is used to find a nonrecursive expansion of a recursive wm filter as applications of theoretical results several interesting deterministic and statistical properties of wm filters are derived

on the structure and geometry of the product singular value decomposition the product singular value decomposition is a factorization of two matrices which can be considered as a generalization of the ordinary singular value decomposition at the same level of generality as the quotient generalized singular value decomposition a constructive proof of the product singular value decomposition is provided which exploits the close relation with a symmetric eigenvalue problem several interesting properties are established the structure and the non uniqueness properties of the so called contragredient transformation which appears as one of the factors in the product singular value decomposition are investigated in detail finally a geometrical interpretation of the structure is provided in terms of principal angles between subspaces

adaptive fuzzy segmentation of magnetic resonance images an algorithm is presented for the fuzzy segmentation of two dimensional 2 d and three dimensional 3 d multispectral magnetic resonance mr images that have been corrupted by intensity inhomogeneities also known as shading artifacts the algorithm is an extension of the 2 d adaptive fuzzy c means algorithm 2 d afcm presented in previous work by the authors this algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space it iteratively adapts to the intensity inhomogeneities and is completely automated in this paper the authors fully generalize 2 d afcm to three dimensional 3 d multispectral images because of the potential size of 3 d image data they also describe a new faster multigrid based algorithm for its implementation they show using simulated mr data that 3 d afcm yields lower error rates than both the standard fuzzy c means fcm algorithm and two other competing methods when segmenting corrupted images its efficacy is further demonstrated using real 3 d scalar and multispectral mr brain images

a robust approach to image enhancement based on fuzzy logic in this paper we propose a robust approach to image enhancement based on fuzzy logic that addresses the seemingly conflicting goals of image enhancement i removing impulse noise ii smoothing out nonimpulse noise and iii enhancing or preserving edges and certain other salient structures we derive three different filters for each of the above three tasks using the weighted or fuzzy least squares ls method and define the criteria for selecting each of the three filters the criteria are based on the local context and they constitute the antecedent clauses of the fuzzy rules the overall result of the fuzzy rule based system is the combination of the results of the individual filters where each result contributes to the degree that the corresponding antecedent clause is satisfied this approach gives us a powerful and flexible image enhancement paradigm results of the proposed method on several types of images are compared with those of other standard techniques