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  • Articles  (64)
  • 1
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    Springer
    Soft computing 4 (2000), S. 76-80 
    ISSN: 1433-7479
    Keywords: Key words Genetic algorithms ; optimization ; numerical methods ; search methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract  In Genetic Algorithms mutation probability is usually assigned a constant value, therefore all chromosome have the same likelihood of mutation irrespective of their fitness. It is shown in this paper that making mutation a function of fitness produces a more efficient search. This function is such that the least significant bits are more likely to be mutated in high-fitness chromosomes, thus improving their accuracy, whereas low-fitness chromosomes have an increased probability of mutation, enhancing their role in the search. In this way, the chance of disrupting a high-fitness chromosome is decreased and the exploratory role of low-fitness chromosomes is best exploited. The implications of this new mutation scheme are assessed with the aid of numerical examples.
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  • 2
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    Journal of systems integration 10 (2000), S. 23-39 
    ISSN: 1573-8787
    Keywords: computer-integrated design ; mechanical design ; engineering methodology ; CAD ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper a methodologyto improve the development of mechanical components during thefirst design phase is proposed. This phase is one of the mostimportant ones in order to build up a product meeting with consumers'approval. The approach is based on a preliminary geometric-structuralmodeling optimization of the component studied. This optimizationis of a multiobjective kind and it aims to obtain some meaningfulgoals to find feasible shapes of the component to design. Themaximization/minimization of the objective functions is performedby an evolutionary algorithm to accomplish a constrained optimizationusing discrete variables. The characteristics of the methodologyare pointed out in relation to the properties of the evolutionaryalgorithm implemented. The paper concludes with an applicationexample of the procedure. The case study is a car gearbox. Threeobjectives are simultaneously pursued: the lowest weight, thegreatest flexural and torsional stiffnesses.
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  • 3
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    Neural processing letters 12 (2000), S. 115-128 
    ISSN: 1573-773X
    Keywords: evolutionary algorithms ; generalization ; learning ; neural networks ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper proposes a new version of a method (G-Prop, genetic backpropagation) that attempts to solve the problem of finding appropriate initial weights and learning parameters for a single hidden layer Multilayer Perceptron (MLP) by combining an evolutionary algorithm (EA) and backpropagation (BP). The EA selects the MLP initial weights, the learning rate and changes the number of neurons in the hidden layer through the application of specific genetic operators, one of which is BP training. The EA works on the initial weights and structure of the MLP, which is then trained using QuickProp; thus G-Prop combines the advantages of the global search performed by the EA over the MLP parameter space and the local search of the BP algorithm. The application of the G-Prop algorithm to several real-world and benchmark problems shows that MLPs evolved using G-Prop are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as QuickPropagation or RPROP, and other evolutive algorithms. It also shows some improvement over previous versions of the algorithm.
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  • 4
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    Journal of automated reasoning 24 (2000), S. 205-223 
    ISSN: 1573-0670
    Keywords: SAT problem ; local search ; meta-heuristics ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, we show how Guided Local Search (GLS) can be applied to the SAT problem and show how the resulting algorithm can be naturally extended to solve the weighted MAX-SAT problem. GLS is a general, penalty-based meta-heuristic, which sits on top of local search algorithms to help guide them out of local minima. GLS has been shown to be successful in solving a number of practical real-life problems, such as the traveling salesman problem, BT"s workforce scheduling problem, the radio link frequency assignment problem, and the vehicle routing problem. We present empirical results of applying GLS to instances of the SAT problem from the DIMACS archive and also a small set of weighted MAX-SAT problem instances and compare them with the results of other local search algorithms for the SAT problem.
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  • 5
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    Computational optimization and applications 16 (2000), S. 159-172 
    ISSN: 1573-2894
    Keywords: optimization ; conjugate gradient method ; global convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper a new nonmonotone conjugate gradient method is introduced, which can be regarded as a generalization of the Perry and Shanno memoryless quasi-Newton method. For convex objective functions, the proposed nonmonotone conjugate gradient method is proved to be globally convergent. Its global convergence for non-convex objective functions has also been studied. Numerical experiments indicate that it is able to efficiently solve large scale optmization problems.
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  • 6
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    International journal of parallel programming 28 (2000), S. 325-345 
    ISSN: 1573-7640
    Keywords: compiler ; optimization ; instruction level parallelism ; code size
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract To achieve high-performance on processors featuring ILP, most compilers apply locally a set of heuristics. This leads to a potentially high-performance on separate code fragments. Unfortunately, most optimizations also increase code size, which may lead to a global net performance loss. In this paper, we propose a Global Constraints-Driven Strategy (GCDS) for guiding code optimization. When using GCDS, the final code optimization decision is taken according to global criteria rather than local criteria. For instance, such criteria might be performance, code size, instruction cache behavior, etc. The performance/code size trade-off is a particularly important problem for embedded systems. We show how GCDS can be used to master code size while optimizing performance.
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  • 7
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    Cybernetics and systems analysis 36 (2000), S. 829-844 
    ISSN: 1573-8337
    Keywords: variational inequalities ; numerical methods ; optimization ; economic equilibrium models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Algorithms for solving finite-dimensional inequalities are studied. The emphasis is on numerical methods based on the optimization approach. Examples of economic equilibrium models that assist in solving variational inequalities are presented.
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  • 8
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    Cybernetics and systems analysis 36 (2000), S. 891-897 
    ISSN: 1573-8337
    Keywords: inventory control system ; controlled Markovian process ; optimization ; convex cost function ; Bellman equation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Some controlled Markovian processes in discrete time in the context of optimization of inventory control systems are studied. Optimality of (s, S)-policies for the case of convex cost functions is proved using theorems on existence and uniqueness of a nonrandomized stationary optimal policy for Markovian processes with discrete time and a continuous control set for criteria characterizing mean costs per unit time and overestimated total costs and Bellman equations.
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  • 9
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    Cybernetics and systems analysis 36 (2000), S. 722-728 
    ISSN: 1573-8337
    Keywords: optimization ; guaranteed state estimation ; dynamic system ; set-valued estimation ; dynamic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The optimization approach to set-theoretic estimation problems is developed. Level sets of strictly convex and continuous functions are used as the set estimates. These functions are shown to satisfy, in estimating, the functional equation of dynamic programming.
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  • 10
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    Constraints 4 (1999), S. 79-89 
    ISSN: 1572-9354
    Keywords: benchmarks ; radio link frequency assignment ; constraint satisfaction ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NP-hard) optimization problem. In 1993, the CELAR (the French “Centre d'Electronique de l'Armement”) built a suite of simplified versions of Radio Link Frequency Assignment Problems (RLFAP) starting from data on a real network Roisnel93. Initially designed for assessing the performances of several Constraint Logic Programming languages, these benchmarks have been made available to the public in the framework of the European EUCLID project CALMA (Combinatorial Algorithms for Military Applications). These problems should look very attractive to the CSP community: the problem is simple to represent, all constraints are binary and involve finite domain variables. They nevertheless have some of the flavors of real problems (including large size and several optimization criteria). This paper gives essential facts about the CELAR instances and also introduces the GRAPH instances which were generated during the CALMA project.
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  • 11
    ISSN: 1572-9354
    Keywords: overconstrained problems ; constraint satisfaction ; optimization ; soft constraint ; dynamic programming ; branch and bound ; complexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we describe and compare two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. While comparing the two approaches, we show how to pass from one to the other one, and we discuss when this is possible. The two frameworks have been independently introduced in ijcai95,jacm and schiex-ijcai95.
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  • 12
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    Constraints 4 (1999), S. 101-112 
    ISSN: 1572-9354
    Keywords: sensitivity analysis ; duality ; optimization ; inference
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The constraint programming community has recently begun to address certain types of optimization problems. These problems tend to be discrete or to have discrete elements. Although sensitivity analysis is well developed for continuous problems, progress in this area for discrete problems has been limited. This paper proposes a general approach to sensitivity analysis that applies to both continuous and discrete problems. In the continuous case, particularly in linear programming, sensitivity analysis can be obtained by solving a dual problem. One way to broaden this result is to generalize the classical idea of a dual to that of an “inference dual,” which can be defined for any optimization problem. To solve the inference dual is to obtain a proof of the optimal value of the problem. Sensitivity analysis can be interpreted as an analysis of the role of each constraint in this proof. This paper shows that traditional sensitivity analysis for linear programming is a special case of this approach. It also illustrates how the approach can work out in a discrete problem by applying it to 0-1 linear programming (linear pseudo-boolean optimization).
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  • 13
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    Journal of intelligent and robotic systems 24 (1999), S. 43-68 
    ISSN: 1573-0409
    Keywords: learning robots ; system organization ; optimization ; physical equation ; look-ut table ; neural networks ; fuzzy controllers
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper explores a stepwise learning approach based on a system's decomposition into functional subsystems. Two case studies are examined: a visually guided robot that learns to track a maneuvering object, and a robot that learns to use the information from a force sensor in order to put a peg into a hole. These two applications show the features and advantages of the proposed approach: i) the subsystems naturally arise as functional components of the hardware and software; ii) these subsystems are building blocks of the robot behavior and can be combined in several ways for performing various tasks; iii) this decomposition makes it easier to check the performances and detect the cause of a malfunction; iv) only those subsystems for which a satisfactory solution is not available need to be learned; v) the strategy proposed for coordinating the optimization of all subsystems ensures an improvement at the task-level; vi) the overall system's behavior is significantly improved by the stepwise learning approach.
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  • 14
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    Journal of intelligent and robotic systems 24 (1999), S. 125-149 
    ISSN: 1573-0409
    Keywords: planning ; control ; multi-fingered robot hand ; optimization ; internal force ; simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In this paper, the problem of controlling multi-fingered robot hands with rolling and sliding contacts is addressed. Several issues are explored. These issues involve the kinematic analysis and modeling, the dynamic analysis and control, and the coordination of a multi-fingered robot hand system. Based on a hand-object system in which the contacts are allowed to both roll and slide, a kinematic model is derived and analyzed. Also, the dynamic model of the hand-object system with relative motion contacts is studied. A control law is proposed to guarantee the asymptotic tracking of the object trajectory together with the desired rolling and/or sliding motions along the surface of the object. A planning approach is then introduced to minimize the contact forces so that the desired motion of the object and the relative motions between the fingers and the object can be achieved. Simulation results which support the theoretical development are presented.
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  • 15
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    Computational economics 13 (1999), S. 103-115 
    ISSN: 1572-9974
    Keywords: optimization ; econometric functions ; nonlinear programming ; evolutionary programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract In the present paper we apply a new Genetic Hybrid Algorithm (GHA) to globally minimize a representative set of ill-conditioned econometric/mathematical functions. The genetic algorithm was specifically designed for nonconvex mixed integer nonlinear programming problems and it can be successfully applied to both global and constrained optimization. In previous studies, we have demonstrated the efficiency of the GHA in solving complicated NLP, INLP and MINLP problems. The present study is a continuation of this research, now focusing on a set of highly irregular optimization problems. In this paper we discuss the genetic hybrid algorithm, the nonlinear problems to be solved and present the results of the empirical tests.
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  • 16
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    Neural processing letters 10 (1999), S. 211-222 
    ISSN: 1573-773X
    Keywords: constraint satisfaction ; Hopfield network ; neural networks ; optimization ; relaxation procedure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract When solving an optimization problem with a Hopfield network, a solution is obtained after the network is relaxed to an equilibrium state. The relaxation process is an important step in achieving a solution. In this paper, a new procedure for the relaxation process is proposed. In the new procedure, the amplified signal received by a neuron from other neurons is treated as the target value for its activation (output) value. The activation of a neuron is updated directly based on the difference between its current activation and the received target value, without using the updating of the input value as an intermediate step. A relaxation rate is applied to control the updating scale for a smooth relaxation process. The new procedure is evaluated and compared with the original procedure in the Hopfield network through simulations based on 200 randomly generated instances of the 10-city traveling salesman problem. The new procedure reduces the error rate by 34.6% and increases the percentage of valid tours by 194.6% as compared with the original procedure.
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  • 17
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    Neural processing letters 9 (1999), S. 119-127 
    ISSN: 1573-773X
    Keywords: reinforcement learning ; neurocontrol ; optimization ; polytope algorithm ; pole balancing ; genetic reinforcement
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorithm to adjust the weights of the action network so that a simple direct measure of the training performance is maximized. Experimental results from the application of the method to the pole balancing problem indicate improved training performance compared with critic-based and genetic reinforcement approaches.
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  • 18
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    International journal of computer vision 34 (1999), S. 19-28 
    ISSN: 1573-1405
    Keywords: geodesic active contours ; active surfaces ; Hamiltonian ; snakes ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Recently, Caselles et al. have shown the equivalence between a classical snake problem of Kass et al. and a geodesic active contour model. The PDE derived from the geodesic problem gives an evolution equation for active contours which is very powerfull for image segmentation since changes of topology are allowed using the level set implementation. However in Caselles' paper the equivalence with classical snake is only shown for 2D images and 1D curves, by using concepts of Hamiltonian theory which have no meanings for active surfaces. This paper propose to examine the notion of equivalence and to revisite Caselles et al. arguments. Then a notion equivalence is introduced and shown for classical snakes and geodesic active contours in the 2D (active contour) and 3D (active surface) case.
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  • 19
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    Computational optimization and applications 12 (1999), S. 41-51 
    ISSN: 1573-2894
    Keywords: convex programming ; optimization ; duality ; error bounds
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Given a single feasible solution $$x_F $$ and a single infeasible solution $$x_I $$ of a mathematical program, we provide an upper bound to the optimal dual value. We assume that $$x_F $$ satisfies a weakened form of the Slater condition. We apply the bound to convex programs and we discuss its relation to Hoffman-like bounds. As a special case, we recover a bound due to Mangasarian [11] on the distance of a point to a convex set specified by inequalities.
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    Design automation for embedded systems 3 (1998), S. 59-73 
    ISSN: 1572-8080
    Keywords: Code generation ; optimization ; digital signal processors
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We address the problem of code optimization for embedded DSP microprocessors. Such processors (e.g., those in the TMS320 series) have highly irregular datapaths, and conventional code generation methods typically result in inefficient code. In this paper we formulate and solve some optimization problems that arise in code generation for processors with irregular datapaths. In addition to instruction scheduling and register allocation, we also formulate the accumulator spilling and mode selection problems that arise in DSP microprocessors. We present optimal and heuristic algorithms that determine an instruction schedule simultaneously optimizing accumulator spilling and mode selection. Experimental results are presented.
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    Queueing systems 30 (1998), S. 273-287 
    ISSN: 1572-9443
    Keywords: single server queue ; controlled service rate ; batch Markovian arrival process ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper deals with the problem of the optimal service rate control in the system with BMAP (Batch Markovian Arrival Process) arrival stream. An algorithm for the computation of the embedded stationary queue length distribution is developed. The procedure for the cost criteria calculation is elaborated for any fixed parameters of the multithreshold control policy.
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    Journal of scientific computing 13 (1998), S. 303-321 
    ISSN: 1573-7691
    Keywords: Infiltration processes ; optimization ; partial differential equations
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A variety of infiltration techniques can be used to fabricate solid materials, particularly composites. In general these processes can be described with at least one time dependent partial differential equation describing the evolution of the solid phase, coupled to one or more partial differential equations describing mass transport through a porous structure. This paper presents a detailed mathematical analysis of a relatively simple set of equations which is used to describe chemical vapor infiltration. The results demonstrate that the process is controlled by only two parameters, α and β. The optimization problem associated with minimizing the infiltration time is also considered. Allowing α and β to vary with time leads to significant reductions in the infiltration time, compared with the conventional case where α and β are treated as constants.
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    Data mining and knowledge discovery 2 (1998), S. 311-324 
    ISSN: 1573-756X
    Keywords: market segmentation ; optimization ; clustering
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a rigorous framework, based on optimization, for evaluating data mining operations such as associations and clustering, in terms of their utility in decision-making. This framework leads quickly to some interesting computational problems related to sensitivity analysis, segmentation and the theory of games.
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    Journal of intelligent and robotic systems 19 (1997), S. 39-54 
    ISSN: 1573-0409
    Keywords: extended Jacobian method ; Lagrange multiplier method ; kinematically redundant manipulators ; numerical efficiency ; optimization ; robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Several methods have been proposed in the past for resolving the control of kinematically redundant manipulators by optimizing a secondary criterion. The extended Jacobian method constrains the gradient of this criterion to be in the null space of the Jacobian matrix, while the Lagrange multiplier method represents the gradient as being in the row space. In this paper, a numerically efficient form of the Lagrange multiplier method is presented and is compared analytically, computationally, and operationally to the extended Jacobian method. This paper also presents an improved method for tracking algorithmic singularities over previous work.
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    Journal of intelligent and robotic systems 19 (1997), S. 89-103 
    ISSN: 1573-0409
    Keywords: kinematics ; manipulator ; optimization ; performance criteria ; redundancy ; robot
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    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A method to simultaneously optimize multiple performance criteria for a redundant manipulator is presented. Preferred formulation requirements for the criteria are discussed, and previously proposed normalization methods are examined. A statistically based variable weighting technique is developed and evaluated. A seven-degree-of-freedom Robotics Research Corporation manipulator is used as a test bed for the multiple criteria optimization scheme.
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    Journal of intelligent and robotic systems 19 (1997), S. 105-117 
    ISSN: 1573-0409
    Keywords: optimization ; Pontryagin’s maximum principle ; redundant manipulators ; state-space augmentation ; trajectory planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The off-line global trajectory planning for kinematically redundant manipulators is formulated as an optimization problem whose solution is obtained by applying the Pontryagin’s Maximum Principle. The state space augmentation method is developed to obtain a set of optimal joint trajectories corresponding to a singularity-free Cartesian path which avoids joint limits and conserves joint configuration in cyclic motion. Results of computer simulation conducted on a three-degree-of-freedom planar manipulator are presented and discussed.
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    Artificial intelligence review 11 (1997), S. 193-225 
    ISSN: 1573-7462
    Keywords: lazy learning ; model selection ; cross validation ; optimization ; attribute selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model with the lowest generalization error is a computationally expensive process, especially if the number of testing points is high or if the number of models is large. Optimization techniques such as hill climbing or genetic algorithms are helpful but can end up with a model that is arbitrarily worse than the best one or cannot be used because there is no distance metric on the space of discrete models. In this paper we develop a technique called “racing” that tests the set of models in parallel, quickly discards those models that are clearly inferior and concentrates the computational effort on differentiating among the better models. Racing is especially suitable for selecting among lazy learners since training requires negligible expense, and incremental testing using leave-one-out cross validation is efficient. We use racing to select among various lazy learning algorithms and to find relevant features in applications ranging from robot juggling to lesion detection in MRI scans.
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    Computational optimization and applications 7 (1997), S. 143-158 
    ISSN: 1573-2894
    Keywords: planning under uncertainty ; parallel computing ; optimization ; software
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear programming and robust optimization. A matrix factorization procedure is employed that exploits the structure of the constraint matrix, and it is implemented on parallel computers. The implementation is perfectly scalable. Extensive computational results are reported for a library of standard test problems from stochastic linear programming, and also for robust optimization formulations.The results show that the codes are efficient and stable for problems with thousands of scenarios. Test problems with 130 thousand scenarios, and a deterministic equivalent linear programming formulation with 2.6 million constraints and 18.2 million variables, are solved successfully.
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    Numerical algorithms 12 (1996), S. 321-346 
    ISSN: 1572-9265
    Keywords: Capillarity ; numerical approximation ; optimization ; 65Dxx ; 65Gxx ; 65K10
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We propose an algorithm to compute an approximation of capillary surfaces in a gravitational field. This algorithm is based on a decomposition-coordination method by augmented lagrangians and the discretization is done using the finite element method. We study the convergence of the algorithm and the error of discretization for the axisymmetric case; some numerical results are given. This method can be generalized to a two-dimensional space.
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    Neural processing letters 3 (1996), S. 139-149 
    ISSN: 1573-773X
    Keywords: Steiner tree ; neural networks ; Hopfield model ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Hopfield neural network model for finding the shortest path between two nodes in a graph was proposed recently in some literatures. In this paper, we present a modified version of Hopfield model to a more general problem of searching an optimal tree (least total cost tree) from a source node to a number of destination nodes in a graph. This problem is called Steiner tree in graph theory, where it is proved to be a NP-complete. Through computer simulations, it is shown that the proposed model could always find an optimal or near-optimal valid solution in various graphs.
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    Journal of network and systems management 4 (1996), S. 133-153 
    ISSN: 1573-7705
    Keywords: High-speed network management ; ATM network management ; multipoint virtual paths design ; optimization ; simulated annealing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Multicast routing over semi-permanent VP (virtual path)s in an ATM (asynchronous transfer mode)-based B-ISDN (broadband integrated services digital network) determines a set of VPs connecting from a source node to destination nodes. The problem of finding the optimal constrained multicasting tree over the semi-permanent VPs in an ATM network is known to be NP (nondeterministic polynomial time)-complete. We develop an optimization methodology for searching a constrained multicast routing tree with minimum cost, using simulated annealing technique. We define the problem-dependent components such as state space and cost function, and refine the implementation-dependent factors including initial temperature and cooling schedule. The simulation results show that our optimization methodology demonstrates good behavior in terms of performance on a variety of graphs modeling the sample ATM networks.
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    Queueing systems 21 (1995), S. 337-389 
    ISSN: 1572-9443
    Keywords: Queueing networks ; loss networks ; multiarmed bandits ; bounds ; policies ; optimization
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    Topics: Computer Science
    Notes: Abstract We survey a new approach that the author and his co-workers have developed to formulate stochastic control problems (predominantly queueing systems) asmathematical programming problems. The central idea is to characterize the region of achievable performance in a stochastic control problem, i.e., find linear or nonlinear constraints on the performance vectors that all policies satisfy. We present linear and nonlinear relaxations of the performance space for the following problems: Indexable systems (multiclass single station queues and multiarmed bandit problems), restless bandit problems, polling systems, multiclass queueing and loss networks. These relaxations lead to bounds on the performance of an optimal policy. Using information from the relaxations we construct heuristic nearly optimal policies. The theme in the paper is the thesis that better formulations lead to deeper understanding and better solution methods. Overall the proposed approach for stochastic control problems parallels efforts of the mathematical programming community in the last twenty years to develop sharper formulations (polyhedral combinatorics and more recently nonlinear relaxations) and leads to new insights ranging from a complete characterization and new algorithms for indexable systems to tight lower bounds and nearly optimal algorithms for restless bandit problems, polling systems, multiclass queueing and loss networks.
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    Queueing systems 20 (1995), S. 207-254 
    ISSN: 1572-9443
    Keywords: Scheduling ; random polling ; optimization ; perturbation analysis ; Golden Ratio policy ; radio network
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    Notes: Abstract We address the problem of schedulingM customer classes in a single-server system, with customers arriving in one ofN arrival streams, as it arises in scheduling transmissions in packet radio networks. In general,N≠M and a customer from some stream may join one of several classes. We consider a slotted time model where at each scheduling epoch the server (channel) is assigned to a particular class (transmission set) and can serve multiple customers (packets) simultaneously, one from every arrival stream (network node) that can belong to this class. The assignment is based on arandom polling policy: the current time slot is allocated to theith class with probability θi. Our objective is to determine the optimal probabilities by adjusting them on line so as to optimize some overall performance measure. We present an approach based on perturbation analysis techniques, where all customer arrival processes can be arbitrary, and no information about them is required. The basis of this approach is the development of two sensitivity estimators leading to amarked slot and aphantom slot algorithm. The algorithms determine the effect of removing/ adding service slots to an existing schedule on the mean customer waiting times by directly observing the system. The optimal slot assignment probabilities are then used to design adeterministic scheduling policy based on the Golden Ratio policy. Finally, several numerical results based on a simple optimization algorithm are included.
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    Computational complexity 5 (1995), S. 155-166 
    ISSN: 1420-8954
    Keywords: Complexity classes ; nondeterministic logspace ; optimization ; iterated multiplication ; 68Q15 ; 68Q25
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    Notes: Abstract We show that computing iterated multiplication of word matrices over {0,1}*, using the operations maximum and concatenation, is complete for the class optL of logspace optimization functions. The same problem for word matrices over {1}* is complete for the class FNL of nondeterministic logspace functions. Improving previously obtained results, we furthermore place the class optL in AC1, and characterize FNL by restricted logspace optimization functions.
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    Machine vision and applications 8 (1995), S. 187-193 
    ISSN: 1432-1769
    Keywords: Tracking ; Segmentation ; Pigs ; Animals ; Computer vision
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    Notes: Abstract An algorithm was developed for the segmentation and tracking of piglets and tested on a 200-image sequence of 10 piglets moving on a straw background. The image-capture rate was 1 image/140 ms. The segmentation method was a combination of image differencing with respect to a median background and a Laplacian operator. The features tracked were blob edges in the segmented image. During tracking, the piglets were modelled as ellipses initialised on the blobs. Each piglet was tracked by searching for blob edges in an elliptical window about the piglet's position, which was predicted from its previous two positions.
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    Statistics and computing 5 (1995), S. 175-190 
    ISSN: 1573-1375
    Keywords: Image analysis ; MAP estimation ; optimization ; simulated annealing ; multi-resolution
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    Topics: Computer Science , Mathematics
    Notes: Abstract We describe an image reconstruction problem and the computational difficulties arising in determining the maximum a posteriori (MAP) estimate. Two algorithms for tackling the problem, iterated conditional modes (ICM) and simulated annealing, are usually applied pixel by pixel. The performance of this strategy can be poor, particularly for heavily degraded images, and as a potential improvement Jubb and Jennison (1991) suggest the cascade algorithm in which ICM is initially applied to coarser images formed by blocking squares of pixels. In this paper we attempt to resolve certain criticisms of cascade and present a version of the algorithm extended in definition and implementation. As an illustration we apply our new method to a synthetic aperture radar (SAR) image. We also carry out a study of simulated annealing, with and without cascade, applied to a more tractable minimization problem from which we gain insight into the properties of cascade algorithms.
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    Autonomous robots 2 (1995), S. 11-27 
    ISSN: 1573-7527
    Keywords: mobile robot ; path ; optimization ; sliding mode ; adaptive control ; tracking strategy
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    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The paper deals with a set of algorithms including path planning, trajectory planning, and path tracking for a tricycle type wheeled mobile robot. Path planning is carried out with parametric polynomial interpolation using an optimization algorithm based on robot geometric constraints. Trajectory characteristics are then derived from the planned geometric path with time varying parameters. A sliding mode control algorithm combined with an adaptive control law are used to track the planned trajectory. The technique deals with an environment free of obstacles. However, it can be easily integrated in a piecewise non colliding path generation. Simulation results are presented to show the validity of the different algorithms.
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    Higher-order and symbolic computation 7 (1994), S. 337-343 
    ISSN: 1573-0557
    Keywords: compiler ; optimization ; loop header ; continuation-passing style ; lambda-calculus ; in-line expansion
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    Topics: Computer Science
    Notes: Abstract The introduction of a “loop header” block facilitates the hoisting of loop-invariant code from a loop. In a λ-calculus intermediate representation, which has a notion of scope, this transformation is particularly useful. Loop headers with scope also solve a problem with in-line expansion of recursive functions or loops: if done naively, only the first iteration is inlined. A loop header can encapsulate the loop or recursion for better in-line expansion. This optimization improves performance by about 5% in Standard ML of New Jersey.
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    The journal of supercomputing 8 (1994), S. 195-207 
    ISSN: 1573-0484
    Keywords: Genetic algorithms ; RNA structure prediction ; massively parallel ; dynamic programming ; optimization
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    Notes: Abstract We present a new method for predicting RNA secondary structure based on a genetic algorithm. The algorithm is designed to run on a massively parallel SIMD computer. Statistical analysis shows that the program performs well when compared to a dynamic programming algorithm used to solve the same problem. The program has also pointed out a long-standing simplification in the implementation of the original dynamic programming algorithm that sometimes causes it not to find the optimal secondary structure.
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    Statistics and computing 4 (1994), S. 131-140 
    ISSN: 1573-1375
    Keywords: Genetic algorithms ; scatter search ; optimization ; mathematical relaxation
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    Topics: Computer Science , Mathematics
    Notes: Abstract We provide a tutorial survey of connections between genetic algorithms and scatter search that have useful implications for developing new methods for optimization problems. The links between these approaches are rooted in principles underlying mathematical relaxations, which became inherited and extended by scatter search. Hybrid methods incorporating elements of genetic algorithms and scatter search are beginning to be explored in the literature, and we demonstrate that the opportunity exists to develop more advanced procedures that make fuller use of scatter search strategies and their recent extensions.
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    Statistics and computing 4 (1994), S. 173-177 
    ISSN: 1573-1375
    Keywords: Maximum likelihood ; mixture models ; simulated annealing ; optimization ; hybrid algorithm
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    Notes: Abstract Traditional (non-stochastic) iterative methods for optimizing functions with multiple optima require a good procedure for selecting starting points. This paper illustrates how the selection of starting points can be made automatically by using a method based upon simulated annealing. We present a hybrid algorithm, possessing the accuracy of traditional routines, whilst incorporating the reliability of annealing methods, and illustrate its performance for a particularly complex practical problem.
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    Mathematical programming 61 (1993), S. 19-37 
    ISSN: 1436-4646
    Keywords: Self-scaling ; BFGS method ; quasi-Newton method ; optimization
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    Notes: Abstract We study the self-scaling BFGS method of Oren and Luenberger (1974) for solving unconstrained optimization problems. For general convex functions, we prove that the method is globally convergent with inexact line searches. We also show that the directions generated by the self-scaling BFGS method approach Newton's direction asymptotically. This would ensure superlinear convergence if, in addition, the search directions were well-scaled, but we show that this is not always the case. We find that the method has a major drawback: to achieve superlinear convergence it may be necessary to evaluate the function twice per iteration, even very near the solution. An example is constructed to show that the step-sizes required to achieve a superlinear rate converge to 2 and 0.5 alternately.
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    Formal methods in system design 3 (1993), S. 25-47 
    ISSN: 1572-8102
    Keywords: mechanical theorem proving ; primitive inference rules ; delayed computation ; optimization
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    Notes: Abstract The HOL system is afully expansive theorem prover: proofs generated in the system are composed of applications of the primitive inference rules of the underlying logic. One can have a high degree of confidence that such systems are sound, but they are far slower than theorem provers that exploit metatheoretic or derived properties. This paper presents techniques for postponing part of the computation so that the user of a fully expansive theorem prover can be more productive. The notions oflazy theorem andlazy conversion are introduced. These not only allow part of the computation to be delayed, but also permit nonlocal optimizations that are only possible because the primitive inferences are not performed immediately. The approach also opens the way to proof procedures that exploit metatheoretic properties without sacrificing security; the primitive inferences still have to be performed in order to generate a theorem, but during the proof development the user is free of the overheads they entail.
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    Queueing systems 14 (1993), S. 369-384 
    ISSN: 1572-9443
    Keywords: Queue ; state dependent parameters ; semi-Markov process ; modulated process ; Markov process ; semi-regenerative process ; ergodic theorems ; steady state distribution ; invariant probability measure ; idle period ; busy period ; input process ; output process ; optimization
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    Notes: Abstract The authors study queueing, input and output processes in a queueing system with bulk service and state dependent service delay. The input flow of customers, modulated by a semi-Markov process, is served by a single server that takes batches of a certain fixed size if available or waits until the queue accumulates enough customers for service. In the latter case, the batch taken for service is of random size dependent on the state of the system, while service duration depends both on the state of the system and on the batch size taken. The authors establish a necessary and sufficient condition for equilibrium of the system and obtain the following results: Explicit formulas for steady state distribution of the queueing process, intensity of the input and output processes, and mean values of idle and busy periods. They employ theory of semi-regenerative processes and illustrate the results by a number of examples. In one of them an optimization problem is discussed.
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    Mathematical programming 61 (1993), S. 281-300 
    ISSN: 1436-4646
    Keywords: Variational inequalities ; descent methods ; optimization
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    Topics: Computer Science , Mathematics
    Notes: Abstract We present a framework for descent algorithms that solve the monotone variational inequality problem VIP v which consists in finding a solutionv *∈Ω v satisfyings(v *)T(v−v *)⩾0, for allv∈Ω v. This unified framework includes, as special cases, some well known iterative methods and equivalent optimization formulations. A descent method is developed for an equivalent general optimization formulation and a proof of its convergence is given. Based on this unified logarithmic framework, we show that a variant of the descent method where each subproblem is only solved approximately is globally convergent under certain conditions.
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    Journal of intelligent and robotic systems 7 (1993), S. 57-71 
    ISSN: 1573-0409
    Keywords: Assembly ; planning ; multi-robot systems ; optimization ; task allocation ; cooperation ; kitting ; heuristics ; cell design ; m-traveling salesmen problem
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    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In previous research, we defined, classified, and solved the robotic assembly plan problem for a single assembly robot. In this paper, we formulate this problem for the case of multiple, cooperating assembly robots. The problem now includes the allocation of assembly tasks among multiple robots.
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    Computational optimization and applications 2 (1993), S. 273-293 
    ISSN: 1573-2894
    Keywords: Large scale ; nonlinear ; optimization ; parallel ; nonhierarchical
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    Topics: Computer Science
    Notes: Abstract Optimizing the design of complex ground and flight vehicles involves multiple disciplines and multilayered computer codes stitched together from mostly incompativle disciplinary codes. The application of established, large-scale, optimization algorithms to the complete model is nearly impossible. Hierarchical decompositions are inappropriate for these types of problems and do not parallelize well. Sobieszczanski-Sobieski has proposed a nonhierarchical decomposition strategyfor nonlinear constrained optimization that is naturally parallel. Despite some successes on engineering problems, the algorithm as originally proposed fails on simple two-dimensional quadratic programs. This paper demonstrates the failure of the algorithm for quadratic programs and suggests a number of possible modifications.
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    Annals of mathematics and artificial intelligence 7 (1993), S. 107-127 
    ISSN: 1573-7470
    Keywords: Logic programs with set ; monotonicity ; subset completion independence ; database query ; optimization
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    Topics: Computer Science , Mathematics
    Notes: Abstract LDL is one of the recently proposed logical query languages, which incorporate set, for data and knowledge base systems. Since LDL programs can simulate negation, they are not monotonic in general. On the other hand, there are monotonic LDL programs. This paper addresses the natural question of “When are the generally nonmonotonic LDL programs monotonic?” and investigates related topics such as useful applications for monotonicity. We discuss four kinds of monotonicity, and examine two of them in depth. The first of the two, called “ω-monotonicity”, is shown to be undecidable even when limited to single-stratum programs. The second, called “uniform monotonicity”, is shown to implyω-monotonicity. We characterize the uniform monotonicity of a program (i) by a relationship between its Bancilhon-Khoshafian semantics and its LDL semantics, and (ii) with a useful property called subset completion independence. Characterization (ii) implies that uniformly monotonie programs can be evaluated more efficiently by discarding dominated facts. Finally, we provide some necessary and/or sufficient, syntactic conditions for uniform monotonicity. The conditions pinpoint (a) enumerated set terms, (b) negations of membership and inclusion, and (c) sharing of set terms as the main source for nonuniform monotonicity.
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    Queueing systems 12 (1992), S. 135-213 
    ISSN: 1572-9443
    Keywords: Queueing networks ; manufacturing systems ; design ; optimization ; second order properties ; convexity ; supermodularity ; transposition increasingness ; Schur convexity
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    Topics: Computer Science
    Notes: Abstract Design issues in various types of manufacturing systems such as flow lines, automatic transfer lines, job shops, flexible machining systems, flexible assembly systems and multiple cell systems are addressed in this paper. Approaches to resolving these design issues of these systems using queueing models are reviewed. In particular, we show how the structural properties that are recently derived for single and multiple stage queueing systems can be used effectively in the solution of certain design optimization problems.
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    Queueing systems 12 (1992), S. 95-133 
    ISSN: 1572-9443
    Keywords: Open queueing networks ; job shops ; approximation procedures ; parametric decomposition approach ; optimization ; Brownian control ; system design ; review
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    Notes: Abstract In this paper we review open queueing network models of manufacturing systems. The paper consists of two parts. In the first part we discuss design and planning problems arising in manufacturing. In doing so we focus on those problems that are best addressed by queueing network models. In the second part of the paper we describe developments in queueing network methodology. We are primarily concerned with features such as general service times, deterministic product routings, and machine failures — features that are prevalent in manufacturing settings. Since these features have eluded exact analysis, approximation procedures have been proposed. In the second part of this paper we review the developments in approximation procedures and highlight the assumptions that underlie these approaches.
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    The journal of supercomputing 6 (1992), S. 49-70 
    ISSN: 1573-0484
    Keywords: CRAY X-MP/Y-MP ; memory conflicts ; optimization ; performance estimation ; vectorization
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    Topics: Computer Science
    Notes: Abstract Optimization of vector-intensive applications for the CRAY X-MP/Y-MP often requires arranging the operations to take full advantage of such architectural features as the memory system, independent memory ports, chaining, and independent functional units. Estimation of performance is not straightforward since many operations can occur concurrently. As a tool for making trades between vector algorithms, a method has been developed and used successfully at E-Systems Inc. to predict the execution time of a sequence of vector operations without resorting to actual code development. This method reduced our software development time, produced significantly more efficient code, and provided for a systematic approach to optimization. The performance estimation is generally accurate to within 10% and accounts for memory conflicts that result from fixed stride references.
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    Computational optimization and applications 1 (1992), S. 277-297 
    ISSN: 1573-2894
    Keywords: Assignment ; auction algorithm ; network programming ; optimization
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    Notes: Abstract In this paper we consider the asymmetric assignment problem and we propose a new auction algorithm for its solution. The algorithm uses in a novel way the recently proposed idea of reverse auction, where, in addition to persons bidding for objects by raising their prices, we also have objects competing for persons by essentially offering discounts. In practice, the new algorithm apparently deals better with price wars than the currently existing auction algorithms. As a result, it tends to terminate substantially (and often dramatically) faster than its competitors.
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    Mathematical programming 52 (1991), S. 11-17 
    ISSN: 1436-4646
    Keywords: Modeling ; cancer ; optimization ; optimal control ; drug delivery
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    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper we consider the problems of modeling the tumor growth and optimize the chemotherapy treatment. A biologically based model is used with the goal of solving an optimization problem involving discrete delivery of antineoplastic drugs. Our model is formulated via compartmental analysis in order to take into account the cell cycle. The cost functional measures not only the final size of the tumor but also the total amount of drug delivered. We propose an algorithm based on the discrete maximum principle to solve the optimal drug schedule problem. Our numerical results show nice interpretations from the medical point of view.
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    Computing 47 (1991), S. 29-42 
    ISSN: 1436-5057
    Keywords: 65K05 ; Interval Newton method ; interval arithmetic ; optimization
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    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Die Arbeit entwickelt ein optimales Intervall-Newton-Verfahren für Systeme von nichtlinearen Gleichungen, wie sie im Zusammenhang mit nichtlinearen Optimierungsaufgaben auftreten. Die Modifikationen des Intervall-Newton-Verfahrens in dieser Arbeit stellen numerisch wirkungsvolle Verbesserungen des allgemeinen Intervall-Newton-Verfahrens dar. In der Arbeit wird aufgezeigt, daß die Berechnung einer optimalen Schrittweite im Intervall-Newton-Verfahren notwendig ist, damit man die Generierung einer feiner werdenden Einschließungsfolge garantieren kann. Dieses Vorgehen wird als optimales Newton-Verfahren bezeichnet und für den mehrdimensionalen Fall implementiert. Die Arbeit zeigt dann die Verwendung des optimalen Intervall-Newton-Verfahrens als “feasible direction”-Verfahren im Zusammenhang mit Nichtnegativitätsbedingungen. In der Implementierung wird auch eine Technik der Matrizenzerlegung verwendet, die den Rechenaufwand für die Inverse der Hesseschen Matrix im Intervall-Newton-Verfahren vermindert. Das Vorgehen wird an mehreren Beispielen demonstriert, u.a. an Optimierungsaufgaben mit Störungen der Problemkoeffizienten. Die numerischen Ergebnisse bestätigen die Leistungsfähigkeit unserer Ansätze.
    Notes: Abstract This paper presents the development of an optimal interval Newton method for systems of nonlinear equations. The context of solving such systems of equations is that of optimization of nonlinear mathematical programs. The modifications of the interval Newton method presented in this paper provide computationally effective enhancements to the general interval Newton method. The paper demonstrates the need to compute an optimal step length in the interval Newton method in order to guarantee the generation of a sequence of improving solutions. This method is referred to as the optimal Newton method and is implemented in multiple dimensions. Secondly, the paper demonstrates the use of the optimal interval Newton method as a feasible direction method to deal with non-negativity constraints. Also, included in this implementation is the use of a matrix decomposition technique to reduce the computational effort required to compute the Hessian inverse in the interval Newton method. The methods are demonstrated on several problems. Included in these problems are mathematical programs with perturbations in the problem coefficients. The numerical results clearly demonstrate the effectiveness and efficiency of these approaches.
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    The journal of supercomputing 5 (1991), S. 31-48 
    ISSN: 1573-0484
    Keywords: C language ; numerical computing ; Fortran ; data type ; floating point arithmetic ; optimization
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    Topics: Computer Science
    Notes: Abstract The predominant programming language for numerical and scientific applications is Fortran, and super-computers are primarily used to run large-scale numerical and scientific applications. C is not widely used for numerical and scientific programming, yet C provides many desirable linguistic features not present in Fortran-77. Furthermore, the existence of a standard library and preprocessor eliminates the worst portability problems. A comparison of C and Fortran-77 shows several key deficiencies in C that reduce its desirability for some numerical problems. Some of these problems have already been addressed by the new ANSI standard for C, but others remain. C with a few extensions and modifications could be suitable for all numerical applications and could become more popular in supercomputing environments.
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    Machine vision and applications 4 (1991), S. 59-87 
    ISSN: 1432-1769
    Keywords: optimization ; feature extraction ; minimal encoding ; generic models
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    Topics: Computer Science
    Notes: Abstract In this paper, we propose a unified optimization framework for feature extraction that lets us simultaneously take into account image data and semantic knowledge: We model objects using a language that specifies both photometric and geometric constraints and defines an information-theoretic objective function that measures the fit of the models to the data. We then treat the problem of finding objects as one of generating the optimal description of the image in terms of this language. We have validated our framework by performing extensive experiments on detecting objects in aerial imagery described by simple geometric constraints and have developed two algorithms for generating optimal descriptions. The first one starts with a rough sketch of a polygonal object and deforms the initial contour to maximize the objective function, thus finding object outlines. The second one automatically extracts complex rectilinear buildings from complex aerial images.
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    Computational economics 4 (1991), S. 15-31 
    ISSN: 1572-9974
    Keywords: Hill-climbing ; maximum likelihood estimation ; non-linear least squares ; optimization
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    Topics: Computer Science , Economics
    Notes: Abstract This article describes EZClimb, a set of SAS/IML steps useful in solving numerical optimization problems. The program uses the method of modified quadratic hill-climbing with either analytical or numerical derivatives to maximize a user-defined criterion function. Modified quadratic hill-climbing is one of the more powerful algorithms known for function optimization but is not widely available outside of the software package GQOPT. The efficacy of the SAS steps is illustrated using Rosenbrock's function, Savin and White's Box-Cox extended autoregressive model, and Klein's Model I.
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    Queueing systems 6 (1990), S. 207-221 
    ISSN: 1572-9443
    Keywords: Batch service queues ; bulk arrival queueing systems ; stochastic control ; optimization ; mass-transit systems
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    Topics: Computer Science
    Notes: Abstract We consider the control of an infinite capacity shuttle which transports passengers between two terminals. The passengers arrive at each terminal according to a compound Poisson process and the travel time from one terminal to the other is a random variable following an arbitrary distribution. The following control limit policy is considered: dispatch the shuttle at terminali, at the instant that the total number of passengers waiting at terminali reaches or exceeds a predetermined control limitm i . The objective of this paper is to obtain the mean waiting time of an arbitrary passenger at each terminal for given control valuesm 1 andm 2. We also discuss a search procedure to obtain the optimal control values which minimize the total expected cost per unit time under a linear cost structure.
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    Machine learning 5 (1990), S. 427-450 
    ISSN: 0885-6125
    Keywords: Learning ; image interpretation ; detection ; optimization ; genetic algorithms ; thresholded rules
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    Topics: Computer Science
    Notes: Abstract We have developed an expert system for interpretation of passive sonar images. A key component of the system is a group of event detection rules whose conditions consist of tests against thresholds. Due to the complexity, variability and clumpiness (i.e., tendency towards highly nonuniform distribution) of the data, tuning these thresholds for good performance under all conditions is a difficult task. We have implemented a procedure for learning rule thresholds whereby the detection capability of each rule continually improves as more and more data is played through the system. The learning procedure contains the following components: 1) a windowing mechanism that adds exceptions (i.e., false alarms and missed detections) into a training database of positive and negative examples and 2) a genetic algorithm to optimize the thresholds with respect to the training database. The genetic training algorithm allows the developer to explicitly choose an operating point on the Receiver Operating Characteristic (ROC) curve of a rule. Experiments have verified 1) the superiority of this automated approach to selecting rule thresholds over manual techniques and 2) the improvement of rule performance with experience.
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    Machine learning 5 (1990), S. 427-450 
    ISSN: 0885-6125
    Keywords: Learning ; image interpretation ; detection ; optimization ; genetic algorithms ; thresholded rules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We have developed an expert system for interpretation of passive sonar images. A key component of the system is a group of event detection rules whose conditions consist of tests against thresholds. Due to the complexity, variability and clumpiness (i.e., tendency towards highly nonuniform distribution) of the data, tuning these thresholds for good performance under all conditions is a difficult task. We have implemented a procedure for learning rule thresholds whereby the detection capability of each rule continually improves as more and more data is played through the system. The learning procedure contains the following components: 1) a windowing mechanism that adds exceptions (i.e., false alarms and missed detections) into a training database of positive and negative examples and 2) a genetic algorithm to optimize the thresholds with respect to the training database. The genetic training algorithm allows the developer to explicitly choose an operating point on the Receiver Operating Characteristic (ROC) curve of a rule. Experiments have verified 1) the superiority of this automated approach to selecting rule thresholds over manual techniques and 2) the improvement of rule performance with experience.
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  • 61
    ISSN: 1572-9443
    Keywords: Effective service time ; age dependent service interruptions ; database systems ; failures ; recovery ; rollback ; checkpoints ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A stochastic model is developed describing a service system subject to inhomogeneous Poisson interruptions with age dependent interruption periods. By studying the probabilistic flow of the underlying multivariate Markov process, the Laplace transform of the effective service time is explicitly obtained. For general renewal interruptions, only the expected effective service time is derived. As an application, an optimal checkpoint policy is examined for database management. It is shown that an optimal policy maximizing the ergodic availability of the database is to implement a checkpoint as soon as the cumulative uptime of the database reaches a prespecified constantk *. A computational procedure is then developed for findingk * and numerical results are exhibited.
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  • 62
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    Computational economics 1 (1988), S. 3-19 
    ISSN: 1572-9974
    Keywords: Decision theory ; optimization ; file search ; algorithm design
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract We investigate the relationship between algorithm construction and optimal decision processes. We provide a sufficient condition, a linear ordering over the experiment set, for when we can efficiently use an optimization approach for selecting a decision strategy. We demonstrate the linear ordering condition within the context of the file search problem; however, any problem whose representation satisfies the linear ordering condition is amenable to the optimization approach.
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  • 63
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    Journal of scientific computing 3 (1988), S. 233-260 
    ISSN: 1573-7691
    Keywords: Computational aerodynamics ; transonic flow ; inverse problem ; wing design ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Conclusion The purpose of the last three sections is to demonstrate by representative examples that control theory can be used to formulate computationally feasible procedures for aerodynamic design. The cost of each iteration is of the same order as two flow solutions, since the adjoint equation is of comparable complexity to the flow equation, and the remaining auxiliary equations could be solved quite inexpensively. Provided, therefore, that one can afford the cost of a moderate number of flow solutions, procedures of this type can be used to derive improved designs. The approach is quite general, not limited to particular choices of the coordinate transformation or cost function, which might in fact contain measures of other criteria of performance such as lift and drag. For the sake of simplicity certain complicating factors, such as the need to include a special term in the mapping function to generate a corner at the trailing edge, have been suppressed from the present analysis. Also it remains to explore the numerical implementation of the design procedures proposed in this paper.
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  • 64
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    International journal of parallel programming 6 (1977), S. 41-54 
    ISSN: 1573-7640
    Keywords: Pattern recognition ; feature selection and extraction ; dimensionality reduction ; data compression ; algorithms ; optimization ; mathematical programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. The general approach used in this procedure was developed in a recent paper by R. J. P. de Figueiredo.(1) Numerical results on the application of this procedure to the remotely sensed data from the Purdue C1 flight line as well asLandsat data are presented. It was found that classification using the optimal single linear feature yielded a value for the probability of misclassification on the order of 30% less than that obtained by using the best single untransformed feature. The optimal single linear feature gave performance results comparable to those obtained by using the two features which maximized the average divergence. Also discussed are improvements in classification results using this method when the size of the training set is small.
    Type of Medium: Electronic Resource
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