Computer search algorithms / Elisabeth C. Salander, editor.

Format:

• Book

Contributor:

• Salander, Elisabeth C.

Series:

• Computer Science, Technology and Applications

Language:

English

Subjects (All):

• Computer algorithms.
• Querying (Computer science).
• Database searching.

Physical Description:

1 online resource (207 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, c2011.

Language Note:

English

Summary:

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.

Contents:

• Intro
• 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
• 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
• 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
• EFFICIENT IMPLEMENTATIONS OF BI-LEVEL PROGRAMMING METHODS FOR CONTINUOUS NETWORK DESIGN PROBLEMS
• 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
• REFERENCES.
• A HYBRID INTELLIGENT TECHNIQUE COMBINES NEURAL NETWORKS AND TABU SEARCH METHODS FOR FORECASTING
• 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
• LU_HANCOCK: A BEST FIRST SEARCH TO PROCESS SINGLE-DESTINATION MULTIPLE-ORIGIN ROUTE QUERY IN A GRAPH
• 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
• The Pseudo Code
• EXPERIMENT AND RESULT ANALYSIS
• Performance Measures
• RESULTS
• 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
• 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
• INDEX.

Notes:

• Description based upon print version of record.
• Includes bibliographical references and index.

ISBN:

1-61209-043-5

OCLC:

831658088