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024 7 _a10.1007/3-540-45322-9
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050 4 _aQA273.A1-274.9
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082 0 4 _a519.2
_223
245 1 0 _aStochastic Algorithms: Foundations and Applications
_h[electronic resource] :
_bInternational Symposium, SAGA 2001 Berlin, Germany, December 13-14, 2001 Proceedings /
_cedited by Kathleen Steinhöfel.
250 _a1st ed. 2001.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2001.
300 _aCCXVI, 208 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v2264
505 0 _aRandomized Communication Protocols -- Optimal Mutation Rate Using Bayesian Priors for Estimation of Distribution Algorithms -- An Experimental Assessment of a Stochastic, Anytime, Decentralized, Soft Colourer for Sparse Graphs -- Randomized Branching Programs -- Yet Another Local Search Method for Constraint Solving -- An Evolutionary Algorithm for the Sequence Coordination in Furniture Production -- Evolutionary Search for Smooth Maps in Motor Control Unit Calibration -- Some Notes on Random Satisfiability -- Prospects for Simulated Annealing Algorithms in Automatic Differentiation -- Optimization and Simulation: Sequential Packing of Flexible Objects Using Evolutionary Algorithms -- Stochastic Finite Learning -- Sequential Sampling Algorithms: Unified Analysis and Lower Bounds -- Approximate Location of Relevant Variables under the Crossover Distribution.
520 _aSAGA 2001, the ?rst Symposium on Stochastic Algorithms, Foundations and Applications, took place on December 13–14, 2001 in Berlin, Germany. The present volume comprises contributed papers and four invited talks that were included in the ?nal program of the symposium. Stochastic algorithms constitute a general approach to ?nding approximate solutions to a wide variety of problems. Although there is no formal proof that stochastic algorithms perform better than deterministic ones, there is evidence by empirical observations that stochastic algorithms produce for a broad range of applications near-optimal solutions in a reasonable run-time. The symposium aims to provide a forum for presentation of original research in the design and analysis, experimental evaluation, and real-world application of stochastic algorithms. It focuses, in particular, on new algorithmic ideas invo- ing stochastic decisions and exploiting probabilistic properties of the underlying problem domain. The program of the symposium re?ects the e?ort to promote cooperation among practitioners and theoreticians and among algorithmic and complexity researchers of the ?eld. In this context, we would like to express our special gratitude to DaimlerChrysler AG for supporting SAGA 2001. The contributed papers included in the proceedings present results in the following areas: Network and distributed algorithms; local search methods for combinatorial optimization with application to constraint satisfaction problems, manufacturing systems, motor control unit calibration, and packing ?exible - jects; and computational learning theory.
650 0 _aProbabilities.
650 0 _aAlgorithms.
650 0 _aComputer science.
650 0 _aComputer science
_xMathematics.
650 0 _aDiscrete mathematics.
650 0 _aMathematical statistics.
650 1 4 _aProbability Theory.
650 2 4 _aAlgorithms.
650 2 4 _aTheory of Computation.
650 2 4 _aDiscrete Mathematics in Computer Science.
650 2 4 _aDiscrete Mathematics.
650 2 4 _aProbability and Statistics in Computer Science.
700 1 _aSteinhöfel, Kathleen.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540430254
776 0 8 _iPrinted edition:
_z9783662212448
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v2264
856 4 0 _uhttps://doi.org/10.1007/3-540-45322-9
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