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