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020    9781612090436|q(electronic bk.) 
020    |z9781611225273 
035    (MiAaPQ)EBC3018963 
035    (Au-PeEL)EBL3018963 
035    (CaPaEBR)ebr10662769 
035    (OCoLC)831658088 
040    MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ 
050  4 QA76.9.A43 -- C673 2011eb 
082 0  005.74/1 
100 1  Salander, Elisabeth C 
245 10 Computer Search Algorithms 
264  1 New York :|bNova Science Publishers, Incorporated,|c2011 
264  4 |c©2011 
300    1 online resource (207 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  Computer Science, Technology and Applications 
505 0  Intro -- COMPUTER SEARCH ALGORITHMS -- COMPUTER SEARCH 
       ALGORITHMS -- CONTENTS -- PREFACE -- LIVE SOFT-MATTER  
       QUANTUM COMPUTING -- ABSTRACT -- INTRODUCTION -- 
       EVOLUTIONARY TRANSITIONS, CONFLICT MEDIATION, AND QUANTUM 
       MECHANICS -- QUANTUM CELL BIOLOGY  AND CELLULAR DECISION 
       MAKING -- MICROBIAL INTELLIGENCES AND LIVE, SOFT MATTER 
       QUANTUM COMPUTING -- DIRECTIONS FOR FUTURE RESEARCH AND 
       DEVELOPMENT OF BIOTECHNOLOGIES -- CONCLUSION -- 
       ACKNOWLEDGMENTS -- REFERENCES -- STUDYING DIFFERENT 
       HEURISTIC SEARCHES TO SOLVE A REAL-WORLD FREQUENCY 
       ASSIGNMENT PROBLEM -- ABSTRACT -- INTRODUCTION -- THE 
       FREQUENCY PLANNING PROBLEM  IN GSM NETWORKS -- 
       Mathematical Description -- HEURISTIC SEARCHES INCLUDED IN
       OUR STUDY -- The Genetic Algorithm -- The Scatter Search 
       Heuristic -- The Population Based Incremental Learning -- 
       The Greedy Randomized Adaptive Search Procedure -- 
       EXPERIMENTAL EVALUATION AND RESULTS -- Empirical Results -
       - CONCLUSION AND FUTURE WORK -- ACKNOWLEDGMENTS -- 
       REFERENCES -- EMERGENCE AND ADVANCES  OF QUANTUM SEARCH --
       BACKGROUND -- AN INTRODUCTION TO QUANTUM COMPUTATION -- 
       Quantum Search Algorithm -- A Quantum Oracle -- Grover's 
       Search Algorithm -- Optimality of Grover's Algorithm -- 
       CONTINUOUS TIME SEARCH ALGORITHM -- Uses of Grover's 
       Search Algorithm -- Hardware Implementation -- CONCLUSION 
       -- ACKNOWLEDGMENTS -- REFERENCES -- EFFICIENT 
       IMPLEMENTATIONS OF BI-LEVEL PROGRAMMING METHODS FOR 
       CONTINUOUS NETWORK DESIGN PROBLEMS -- ABSTRACT -- 1. 
       INTRODUCTION -- 2. BI-LEVEL PROGRAMMING PROBLEM (BLPP) 
       FORMULATION FOR ENDP -- 3. SOLUTION ALGORITHMS -- 3.1. 
       Rosen's Gradient Projection Method -- 3.2. Conjugate 
       Gradient Projection Method -- 3.3. Quasi-Newton Projection
       Method: Algorithm of BFGS -- 3.4. Rosen's Gradient 
       Projection Method with PARTAN -- 4. COMPUTATIONAL RESULTS 
       -- CONCLUSIONS AND DISCUSSIONS -- ACKNOWLEDGMENTS -- 
       REFERENCES 
505 8  A HYBRID INTELLIGENT TECHNIQUE COMBINES NEURAL NETWORKS 
       AND TABU SEARCH METHODS FOR FORECASTING -- ABSTRACT -- 1. 
       INTRODUCTION -- 2. ARTIFICIAL NEURAL NETWORKS -- 3. THE 
       HYBRID INTELLIGENT TECHNIQUE FOR FORECASTING -- 3.1. The 
       Tabu Search Algorithm -- 3.2. The Hybrid Intelligent 
       Method for Forecasting -- 4. IMPLEMENTATION -- CONCLUSION 
       -- REFERENCES -- LU_HANCOCK: A BEST FIRST SEARCH TO 
       PROCESS SINGLE-DESTINATION MULTIPLE-ORIGIN ROUTE QUERY IN 
       A GRAPH -- ABSTRACT -- INTRODUCTION -- RELATED WORK -- LU:
       A BEST FIRST SEARCH ALGORITHM TO PROCESS SOMDR QUERIES IN 
       A GRAPH -- Algorithm -- Admissibility and Optimality -- 
       LU_HANCOCK: THE REVERSE LU TO PROCESS SDMOR QUERIES IN A 
       GRAPH -- Algorithm -- Admissibility and Optimality -- The 
       Pseudo Code -- EXPERIMENT AND RESULT ANALYSIS -- 
       Performance Measures -- RESULTS -- CONCLUSION -- 
       REFERENCES -- SOME HEURISTIC APPROACHES FOR SOLVING NON-
       CONVEX OPTIMIZATION PROBLEMS -- Abstract -- 1.Introduction
       -- 2.Stochastic methods for solving continuous non-convex 
       optimization problems -- 2.1.Simulated annealing -- 
       2.1.1.Metropolis algorithm and simulated annealing -- 
       2.1.2.Simulated annealing algorithm -- 2.2.Genetic 
       Algorithm -- 2.2.1.The main steps of a Genetic Algorithm -
       - 2.2.2.The standard genetic algorithm -- 2.3.Particle 
       Swarm Optimization (PSO) -- 2.3.1.Dynamics of the 
       particles of a swarm -- 2.3.2.The standard PSO algorithm -
       - 2.4.Heuristic Kalman Algorithm -- 2.4.1.Principle of the
       algorithm -- 2.4.2.The updating rule of the Gaussian 
       generator -- 2.4.3.Algorithm -- 3.Quasi Geometric 
       Programming -- 3.1.Geometric Programming -- 3.1.1.Standard
       formulation -- 3.1.2.Convex formulation -- 3.2.Formulation
       of a Quasi Geometric Programming Problem -- 3.3.Resolution
       of a QGP -- 3.4.Robustness Issue -- 4.Application to Some 
       Engineering Problems -- 4.1.Robust Structured Control 
505 8  4.1.1.Formulation of the optimization problem -- 
       4.1.2.Numerical experiments -- 4.2.Design of Spiral 
       Inductors on Silicon -- 4.2.1.Inductor model -- 
       4.2.2.Formulation of the optimization problem -- 
       4.2.3.Numerical experiments -- 5.Conclusion -- References 
       -- EVOLUTIONARY ALGORITHM BASED ON CONCEPT OF STOCHASTIC 
       SCHEMATA EXPLOITER -- Abstract -- 1.Introduction -- 2.Real
       -Coded Genetic Algorithms -- 2.1.Optimization Problem -- 
       2.2.RGA Algorithm -- 2.3.Simplex Crossover (SPX) -- 
       2.4.Unimodal Normal Distribution Crossover (UNDX-m) -- 
       2.5.Minimum Generation Gap -- 3.Real-Coded Stochastic 
       Schemata Exploiter (RSSE) -- 3.1.RSSE Algorithm -- 
       3.2.Defining Sub-populations -- 3.2.1.Semi-Order Relation 
       -- 3.2.2.Sub-population -- 4.Numerical Examples -- 
       4.1.Test Problems -- 4.1.1.Sphere Function -- 
       4.1.2.Rastrigin Function -- 4.1.3.Schwefel Function -- 
       4.1.4.Ridge Function -- 4.1.5.Rosenbrock Function -- 
       4.1.6.Griewank Function -- 4.2.Numerical Results -- 
       4.2.1.Sphere Function -- 4.2.2.Rastrigin Function -- 
       4.2.3.Schwefel Function -- 4.2.4.Ridge Function -- 
       4.2.5.Rosenbrock Function -- 4.2.6.Griewank Function -- 
       5.Conclusion -- References -- INDEX 
588    Description based on publisher supplied metadata and other
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590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
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650  0 Computer algorithms.;Querying (Computer science);Database 
       searching 
655  4 Electronic books 
700 1  Salander, Elisabeth C 
776 08 |iPrint version:|aSalander, Elisabeth C.|tComputer Search 
       Algorithms|dNew York : Nova Science Publishers, 
       Incorporated,c2011|z9781611225273 
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