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  • 1
    Electronic Resource
    Electronic Resource
    Adaptive mutation in genetic algorithms (2000)
    Springer
    Soft computing 4 (2000), S. 76-80 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s005000000042
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  • 2
    Electronic Resource
    Electronic Resource
    Preliminary Optimal Geometric Modeling of Mechanical Components: A Multiobjective Approach (2000)
    Springer
    Journal of systems integration 10 (2000), S. 23-39 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1026555200788
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  • 3
    Electronic Resource
    Electronic Resource
    Evolving Multilayer Perceptrons (2000)
    Springer
    Neural processing letters 12 (2000), S. 115-128 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009684907680
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  • 4
    Electronic Resource
    Electronic Resource
    Guided Local Search for Solving SAT and Weighted MAX-SAT Problems (2000)
    Springer
    Journal of automated reasoning 24 (2000), S. 205-223 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006343127545
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  • 5
    Electronic Resource
    Electronic Resource
    Convergence of the Nonmonotone Perry and Shanno Method for Optimization (2000)
    Springer
    Computational optimization and applications 16 (2000), S. 159-172 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008753308646
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  • 6
    Electronic Resource
    Electronic Resource
    Handling Global Constraints in Compiler Strategy (2000)
    Springer
    International journal of parallel programming 28 (2000), S. 325-345 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007502921104
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  • 7
    Electronic Resource
    Electronic Resource
    Models and Methods of Finite-Dimensional Variational Inequalities (2000)
    Springer
    Cybernetics and systems analysis 36 (2000), S. 829-844 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009453225995
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  • 8
    Electronic Resource
    Electronic Resource
    Optimal Strategies for Inventory Control Systems with a Convex Cost Function (2000)
    Springer
    Cybernetics and systems analysis 36 (2000), S. 891-897 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009413511883
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  • 9
    Electronic Resource
    Electronic Resource
    Optimization Approach to Guaranteed State Estimation of Dynamic Systems (2000)
    Springer
    Cybernetics and systems analysis 36 (2000), S. 722-728 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009480823460
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