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1. |
Record Nr. |
UNISALENTO991002057909707536 |
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Autore |
Sanchis, Josemaría |
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Titolo |
La legge penale e il precetto penale / Josemaría Sanchis |
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Pubbl/distr/stampa |
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Milano : A. Giuffrè, 1993 |
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ISBN |
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Descrizione fisica |
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Collana |
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Monografie giuridiche dell'Ateneo romano della Santa Croce ; 7 |
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Classificazione |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNISALENTO991001819959707536 |
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Titolo |
Advances in applied mathematics [e-journal]. - 1980- |
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ISSN |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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Pubblicato anche in formato cartaceo |
Accesso elettronico: LE013 1980- |
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3. |
Record Nr. |
UNINA9910965271203321 |
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Titolo |
Computer search algorithms / / Elisabeth C. Salander, editor |
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Pubbl/distr/stampa |
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Hauppauge, N.Y., : Nova Science Publishers, c2011 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (207 p.) |
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Collana |
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Computer Science, Technology and Applications |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Computer algorithms |
Querying (Computer science) |
Database searching |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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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. |
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. |
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 -- |
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5.Conclusion -- References -- INDEX. |
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Sommario/riassunto |
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In computer science, a search algorithm, is an algorithm for finding an item with specified properties among a collection of items. The items may be stored individually as records in a database; or may be elements of a search space defined by a mathematical formula or procedure, such as the roots of an equation with integer variables; or a combination of the two, such as the Hamiltonian circuits of a graph. This book presents research data in the study of computer search algorithms, including live soft-matter quantum computing; heuristic searches applied to the resolution of a relevant optimization problem from the telecommunications domain; the emergence and advances of quantum search algorithms; an equilibrium network design problem for road traffic network; artificial neural networks; and evolutionary algorithms based on the concept of stochastic schemata exploiter. |
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