BibSonomy publications for /concept/tag/distributionSun Aug 17 00:20:25 CEST 2008New York, NY, USAANCS '07: Proceedings of the 3rd ACM/IEEE Symposium on Architecture for networking and communications systems67--76Automated Task Distribution in Multicore Network Processors using Statistical Analysis2007PhD Distribution MultiCore Tasks Proposal Sun Jul 20 17:59:41 CEST 20081. Krypto-Tag -- Workshop über Kryptographie10Group Authentication and Encryption in Distributed EnvironmentsTR 20042004encryption authentication phbaer-pub year:2004 distribution robot group Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesMarch153--84Sporadic model building for efficiency enhancement of the hierarchical {BOA}92008model Efficiency algorithms, Hierarchical BOA, building optimisation of Estimation Sporadic distribution enhancement, HBOA, Bayesian genetic algorithm, Efficiency enhancement techniques such as parallelisation and hybridisation are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyses sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimisation algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of $$\Uptheta(n^{0.26})$$ to $$\Uptheta(n^{0.5}),$$ where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structure-building period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.Thu Jun 19 17:46:40 CEST 2008London, UK7-11 July{GECCO} 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation2007Robotics, Estimation Multiobjective algorithms, Applications, Intelligence, Search-Based Systems, Strategies, Optimisation, and Swarm Real-World Evolutionary Immune Formal Hardware, of Coevolution, Evolution Theory, Software Distribution genetic Learning, programming, Programming, Artificial Adaptive Algorithms, Behaviour, Developmental Biological Genetics-Based Engineering Colony Machine Generative Life, Ant Evolvable Thu Jun 19 17:35:00 CEST 2008Washington DC, USA25-29 June{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation2005Search-based Real Behaviour, Adaptive Ant Strategies, Distribution Biological Immune Evolvable Hardware, Engineering Intelligence, Optimization, Artificial Software Optimisation and World Robotics Applications, Algorithms, A-Life, Evolutionary Search, genetic Combinatorial Colony Estimation Swarm of Programming, programming, Optimisation, algorithms, Coevolution, Multi-objective Meta-heuristics Local Systems, The papers in this two volume proceedings are presented at the 7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005), held in Washington, D.C., June 25-29, 2005.This year is an exceptional one for the GECCO conference series. First, the International Society for Genetic and Evolutionary Computation (ISGEC) which has always been GECCO's sponsor has changed to become a Special Interest Group of the ACM named SIGEVO. Being part of ACM reflects the evolution and integration of our very successful discipline into the main stream of computer science. As a consequence, the GECCO-2005 proceedings are an ACM publication and they are incorporated into the ACM Digital Library. This guarantees an even broader dissemination of Darwinian and other nature-inspired computation methods.Second, we had 549 regular paper submissions representing the absolute record of all conferences emphasising the field of evolutionary computation. Paper reviewing has been done by double blind assignment. On average each paper was evaluated by five independent reviewers. Finally, 253 paper (46.1%) have been accepted as full (max. 8 pages) papers. Additionally, 120 submissions were accepted as posters.A goal of GECCO is to encourage new areas and paradigms of evolutionary computation to gather momentum and flourish. This is accomplished by the establishment of new independent tracks each year. This year, as a result of a recombinative and creative process, GECCO-2005 comprises 16 tracks consisting of core tracks ({"}C{"}), tracks previously in GECCOs ({"}P{"}), not yet belonging to the core track family), {"}recombined{"} tracks from GECCO 2004 ({"}R{"}), and newly created tracks ({"}N{"}):.Thu Jun 19 17:35:00 CEST 2008Adelard, London, UKIEEE Transactions on ReliabilityDecember4550--560Conservative theory for long-term reliability-growth prediction [of software]451996theory, worst-case bound, software modeling, rate reliability, prediction, failure residual number, program fault long-term distribution growth use-time, rate, initial reliability reliability-growth analysis, This paper describes a different approach to software reliability growth modeling which enables long-term predictions. Using relatively common assumptions, it is shown that the average value of the failure rate of the program, after a particular use-time, t, is bounded by N/(e/spl middot/t), where N is the initial number of faults. This is conservative since it places a worst-case bound on the reliability rather than making a best estimate. The predictions might be relatively insensitive to assumption violations over the longer term. The theory offers the potential for making long-term software reliability growth predictions based solely on prior estimates of the number of residual faults. The predicted bound appears to agree with a wide range of industrial and experimental reliability data. Less pessimistic results can be obtained if additional assumptions are made about the failure rate distribution of faults.Thu Jun 19 17:35:00 CEST 2008LondonGECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation7-11 July1588--1595Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat22007initialisation, programming, genetic bloat, algorithms, Size Bias, program crossover distribution Recent research [1] has found that standard sub-tree crossover with uniform selection of crossover points, in the absence of fitness pressure, pushes a population of GP trees towards a Lagrange distribution of tree sizes. However, the result applied to the case of single arity function plus leaf node combinations, e.g., unary, binary, ternary, etc trees only. In this paper we extend those findings and show that the same distribution is also applicable to the more general case where the function set includes functions of mixed arities. We also provide empirical evidence that strongly corroborates this generalisation. Both predicted and observed results show a distinct bias towards the sampling of shorter programs irrespective of the mix of function arities used. Practical applications and implications of this knowledge are investigated with regard to search efficiency and program bloat. Work is also presented regarding the applicability of the theory to the traditional 0.90-function 0.10-terminal crossover node selection policy.Thu Jun 19 17:35:00 CEST 2008Washington DC, USA{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation25-29 June747--748Learning computer programs with the bayesian optimization algorithm12005programming, study, design, of empirical representations Poster, Estimation Algorithms, genetic algorithms, Distribution Thu Jun 19 17:35:00 CEST 2008LondonGECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation7-11 July539--546Scalable estimation-of-distribution program evolution12007empirical heuristics, Study, programming, genetic optimisation, algorithms, representation Estimation of Distribution Algorithms, I present a new estimation-of-distribution approach to program evolution where distributions are not estimated over the entire space of programs. Rather, a novel representation-building procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This leads to a system of demes consisting of alternative representations (i.e. program subspaces) that are maintained simultaneously and managed by the overall system. Meta-optimising semantic evolutionary search (MOSES), a program evolution system based on this approach, is described, and its representation-building subcomponent is analysed in depth. Experimental results are also provided for the overall MOSES procedure that demonstrate good scalability.Thu Jun 19 17:35:00 CEST 2008IEEE Transactions on Evolutionary ComputationApril164--72Interval-valued {GA}-{P} algorithms42000energy genetic symbolic estimate, rural spanish regression, distribution point interval, algorithms, programming, confidence electrical When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on interval GA-P can devise an interval model from examples and provide the algebraic expression that best approximates the data. The method is useful for generating a confidence interval for the output of a model, and also for obtaining a robust point estimate from data which we know to contain outliers. The algorithm was applied to a real problem related to electrical energy distribution. Classical methods were applied first, and then the interval GA-P. The results of both studies are used to compare interval GA-P with GP, GA-P, classical regression methods, neural networks, and fuzzy models.Thu Jun 19 17:35:00 CEST 2008Ecological Modelling15 MaySelected Papers from the Third Conference of the International Society for Ecological Informatics (ISEI), August 26--30, 2002, Grottaferrata, Rome, Italy1-2129--138Machine learning of poorly predictable ecological data1952006Spatial bandicoot, vector brown programming, algorithms, networks, Decision Neural Support modelling genetic trees, distribution machines, Southern a variety of machine learning techniques to a difficult modelling problem, the spatial distribution of an endangered Australian marsupial, the southern brown bandicoot (Isoodon obesulus). Four learning techniques decision trees/rules, neural networks, support vector machines and genetic programming were applied to the problem. Support vector and neural network approaches gave marginally better predictivity, but in the context of low overall accuracy, decision trees and genetic programming gave more useful results because of the human comprehensibility of their models.Thu Jun 19 17:35:00 CEST 2008New ZealandCS-TR-04-7Probability Based Genetic Programming for Multiclass Object Classification2004Gaussian classification Probability multiclass distribution distribution, distance, genetic area, object based weighted algorithms, programming, overlap Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian distributions, to construct the fitness function for classification. Two fitness measures, overlap area and weighted distribution distance, have been developed. The approach is examined on three multiclass object classification problems of increasing difficulty and compared with a basic GP approach. The results suggest that the new approach is more effective and more efficient than the basic GP approach. While the area measure was a bit more effective than the distance measure in most cases, the distance measure was more efficient to learn good program classifiers.Thu Jun 19 17:35:00 CEST 2008Washington DC, USA{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation25-29 June687--694Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot12005Algorithms, algorithms, locomotion, grammar, genetic strategies, Distribution programming, design, of mutation context-sensitive Estimation snakebot Thu Jun 19 17:35:00 CEST 2008Washington DC, USA{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation25-29 June1775--1776Probabilistic distribution models for {EDA}-based {GP}22005probabilistic building evolution, algorithm, Poster, of programming, PIPE, evolutionary model algorithms, XEDP estimation genetic program building, computing, distribution Thu Jun 12 16:57:16 CEST 2008The European Physical Journal B - Condensed Matter and Complex SystemsStochastic dynamics of a sheared granular medium2008imported extreme 2008 granular stress statphys proceeding distribution universality myown friction Abstract. We experimentally investigate the response of a sheared granular medium in a Couette geometry. The apparatus exhibits the expected stick-slip motion and we probe it in the very intermittent regime resulting from low driving. Statistical analysis of the dynamic fluctuations reveals notable regularities. We observe a possible stability property for the torque distribution, reminiscent of the stability of Gaussian independent variables. In this case, however, the variables are correlated and the distribution is skewed. Moreover, the whole dynamical intermittent regime can be described with a simple stochastic model, finding good quantitative agreement with the experimental data. Interestingly, a similar model has been previously introduced in the study of magnetic domain wall motion, a source of Barkhausen noise. Our study suggests interesting connections between different complex phenomena and reveals some unexpected features that remain to be explained. Tue May 27 09:42:10 CEST 2008The European Physical Journal B - Condensed Matter and Complex Systems2255-258Fitting to the power-law distribution412004distribution power law powerlaw fitting Version 1 of Goldstein 04 power law fit containing also the chi 2 testWed May 14 11:40:54 CEST 2008Contemporary Physics323Power laws, Pareto distributions and Zipf's law462005tail zipf scale power free long law distribution Mon Apr 28 14:02:50 CEST 2008Journal of Policy Modeling004553--566Economic development and income distribution292007Income distribution Mon Apr 28 14:02:50 CEST 2008Journal of Policy Modeling004567--575National welfare and individual happiness: Income distribution and beyond292007distribution Income Mon Apr 28 14:02:50 CEST 2008Journal of Policy Modeling004623--633Income inequality and income taxation292007distribution Pre-tax income