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Optimization Techniques for Solving Complex Problems :Optimization Techniques for Solving Complex Problems.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Optimization Techniques for Solving Complex Problems :/
其他題名:	Optimization Techniques for Solving Complex Problems.
作者:	Alba, Enrique.
其他作者:	Blum, Christian.
面頁冊數:	1 online resource (502 pages)
標題:	Computer science -- Mathematics.;Mathematical optimization.;Problem solving. -
電子資源:	Click to View - 點擊此處查看電子書全文
ISBN:	9780470411346

Optimization Techniques for Solving Complex Problems :Optimization Techniques for Solving Complex Problems.
Alba, Enrique.

Optimization Techniques for Solving Complex Problems :Optimization Techniques for Solving Complex Problems. - 1st ed. - 1 online resource (502 pages) - Wiley Series on Parallel and Distributed Computing Ser. ;v.76. - Wiley Series on Parallel and Distributed Computing Ser..

Intro -- OPTIMIZATION TECHNIQUES FOR SOLVING COMPLEX PROBLEMS -- CONTENTS -- CONTRIBUTORS -- FOREWORD -- PREFACE -- PART I METHODOLOGIES FOR COMPLEX PROBLEM SOLVING -- 1 Generating Automatic Projections by Means of Genetic Programming -- 1.1 Introduction -- 1.2 Background -- 1.3 Domains -- 1.4 Algorithmic Proposal -- 1.5 Experimental Analysis -- 1.6 Conclusions -- References -- 2 Neural Lazy Local Learning -- 2.1 Introduction -- 2.2 Lazy Radial Basis Neural Networks -- 2.3 Experimental Analysis -- 2.4 Conclusions -- References -- 3 Optimization Using Genetic Algorithms with Micropopulations -- 3.1 Introduction -- 3.2 Algorithmic Proposal -- 3.3 Experimental Analysis: The Rastrigin Function -- 3.4 Conclusions -- References -- 4 Analyzing Parallel Cellular Genetic Algorithms -- 4.1 Introduction -- 4.2 Cellular Genetic Algorithms -- 4.3 Parallel Models for cGAs -- 4.4 Brief Survey of Parallel cGAs -- 4.5 Experimental Analysis -- 4.6 Conclusions -- References -- 5 Evaluating New Advanced Multiobjective Metaheuristics -- 5.1 Introduction -- 5.2 Background -- 5.3 Description of the Metaheuristics -- 5.4 Experimental Methodology -- 5.5 Experimental Analysis -- 5.6 Conclusions -- References -- 6 Canonical Metaheuristics for Dynamic Optimization Problems -- 6.1 Introduction -- 6.2 Dynamic Optimization Problems -- 6.3 Canonical MHs for DOPs -- 6.4 Benchmarks -- 6.5 Metrics -- 6.6 Conclusions -- References -- 7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms -- 7.1 Introduction -- 7.2 Strategies for Solving CCOPs with HEAs -- 7.3 Study Cases -- 7.4 Conclusions -- References -- 8 Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques -- 8.1 Introduction -- 8.2 Time Series Identification -- 8.3 Optimization Problem -- 8.4 Algorithmic Proposal -- 8.5 Experimental Analysis -- 8.6 Conclusions -- References.

Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One-covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two-delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.

ISBN: 9780470411346Subjects--Topical Terms:

1000121087
Computer science -- Mathematics.;Mathematical optimization.;Problem solving.
Index Terms--Genre/Form:

172687
Electronic books.

LC Class. No.: QA76.9.M35 -- O78 2009eb

Dewey Class. No.: 004.01/51

Optimization Techniques for Solving Complex Problems :Optimization Techniques for Solving Complex Problems.
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505 0 $a Intro -- OPTIMIZATION TECHNIQUES FOR SOLVING COMPLEX PROBLEMS -- CONTENTS -- CONTRIBUTORS -- FOREWORD -- PREFACE -- PART I METHODOLOGIES FOR COMPLEX PROBLEM SOLVING -- 1 Generating Automatic Projections by Means of Genetic Programming -- 1.1 Introduction -- 1.2 Background -- 1.3 Domains -- 1.4 Algorithmic Proposal -- 1.5 Experimental Analysis -- 1.6 Conclusions -- References -- 2 Neural Lazy Local Learning -- 2.1 Introduction -- 2.2 Lazy Radial Basis Neural Networks -- 2.3 Experimental Analysis -- 2.4 Conclusions -- References -- 3 Optimization Using Genetic Algorithms with Micropopulations -- 3.1 Introduction -- 3.2 Algorithmic Proposal -- 3.3 Experimental Analysis: The Rastrigin Function -- 3.4 Conclusions -- References -- 4 Analyzing Parallel Cellular Genetic Algorithms -- 4.1 Introduction -- 4.2 Cellular Genetic Algorithms -- 4.3 Parallel Models for cGAs -- 4.4 Brief Survey of Parallel cGAs -- 4.5 Experimental Analysis -- 4.6 Conclusions -- References -- 5 Evaluating New Advanced Multiobjective Metaheuristics -- 5.1 Introduction -- 5.2 Background -- 5.3 Description of the Metaheuristics -- 5.4 Experimental Methodology -- 5.5 Experimental Analysis -- 5.6 Conclusions -- References -- 6 Canonical Metaheuristics for Dynamic Optimization Problems -- 6.1 Introduction -- 6.2 Dynamic Optimization Problems -- 6.3 Canonical MHs for DOPs -- 6.4 Benchmarks -- 6.5 Metrics -- 6.6 Conclusions -- References -- 7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms -- 7.1 Introduction -- 7.2 Strategies for Solving CCOPs with HEAs -- 7.3 Study Cases -- 7.4 Conclusions -- References -- 8 Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques -- 8.1 Introduction -- 8.2 Time Series Identification -- 8.3 Optimization Problem -- 8.4 Algorithmic Proposal -- 8.5 Experimental Analysis -- 8.6 Conclusions -- References.
505 8 $a 9 Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms -- 9.1 Introduction -- 9.2 Description of the Cryptographic Algorithms -- 9.3 Implementation Proposal -- 9.4 Expermental Analysis -- 9.5 Conclusions -- References -- 10 Genetic Algorithms, Parallelism, and Reconfigurable Hardware -- 10.1 Introduction -- 10.2 State of the Art -- 10.3 FPGA Problem Description and Solution -- 10.4 Algorithmic Proposal -- 10.5 Experimental Analysis -- 10.6 Conclusions -- References -- 11 Divide and Conquer: Advanced Techniques -- 11.1 Introduction -- 11.2 Algorithm of the Skeleton -- 11.3 Experimental Analysis -- 11.4 Conclusions -- References -- 12 Tools for Tree Searches: Branch-and-Bound and A* Algorithms -- 12.1 Introduction -- 12.2 Background -- 12.3 Algorithmic Skeleton for Tree Searches -- 12.4 Experimentation Methodology -- 12.5 Experimental Results -- 12.6 Conclusions -- References -- 13 Tools for Tree Searches: Dynamic Programming -- 13.1 Introduction -- 13.2 Top-Down Approach -- 13.3 Bottom-Up Approach -- 13.4 Automata Theory and Dynamic Programming -- 13.5 Parallel Algorithms -- 13.6 Dynamic Programming Heuristics -- 13.7 Conclusions -- References -- PART II APPLICATIONS -- 14 Automatic Search of Behavior Strategies in Auctions -- 14.1 Introduction -- 14.2 Evolutionary Techniques in Auctions -- 14.3 Theoretical Framework: The Ausubel Auction -- 14.4 Algorithmic Proposal -- 14.5 Experimental Analysis -- 14.6 Conclusions -- References -- 15 Evolving Rules for Local Time Series Prediction -- 15.1 Introduction -- 15.2 Evolutionary Algorithms for Generating Prediction Rules -- 15.3 Experimental Methodology -- 15.4 Experiments -- 15.5 Conclusions -- References -- 16 Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction -- 16.1 Introduction -- 16.2 Metaheuristics and Bioinformatics -- 16.3 DNA Fragment Assembly Problem.
505 8 $a 16.4 Shortest Common Supersequence Problem -- 16.5 Conclusions -- References -- 17 Optimal Location of Antennas in Telecommunication Networks -- 17.1 Introduction -- 17.2 State of the Art -- 17.3 Radio Network Design Problem -- 17.4 Optimization Algorithms -- 17.5 Basic Problems -- 17.6 Advanced Problem -- 17.7 Conclusions -- References -- 18 Optimization of Image-Processing Algorithms Using FPGAs -- 18.1 Introduction -- 18.2 Background -- 18.3 Main Features of FPGA-Based Image Processing -- 18.4 Advanced Details -- 18.5 Experimental Analysis: Software Versus FPGA -- 18.6 Conclusions -- References -- 19 Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics -- 19.1 Introduction -- 19.2 Background -- 19.3 Laser Dynamics Problem -- 19.4 Algorithmic Proposal -- 19.5 Experimental Analysis -- 19.6 Parallel Implementation of the Algorithm -- 19.7 Conclusions -- References -- 20 Dense Stereo Disparity from an Artificial Life Standpoint -- 20.1 Introduction -- 20.2 Infection Algorithm with an Evolutionary Approach -- 20.3 Experimental Analysis -- 20.4 Conclusions -- References -- 21 Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems -- 21.1 Introduction -- 21.2 Multidimensional Knapsack Problem -- 21.3 Hybrid Models -- 21.4 Experimental Analysis -- 21.5 Conclusions -- References -- 22 Greedy Seeding and Problem-Specific Operators for Gas Solution of Strip Packing Problems -- 22.1 Introduction -- 22.2 Background -- 22.3 Hybrid GA for the 2SPP -- 22.4 Genetic Operators for Solving the 2SPP -- 22.5 Initial Seeding -- 22.6 Implementation of the Algorithms -- 22.7 Experimental Analysis -- 22.8 Conclusions -- References -- 23 Solving the KCT Problem: Large-Scale Neighborhood Search and Solution Merging -- 23.1 Introduction -- 23.2 Hybrid Algorithms for the KCT Problem -- 23.3 Experimental Analysis.
505 8 $a 23.4 Conclusions -- References -- 24 Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments -- 24.1 Introduction -- 24.2 Related Work -- 24.3 Independent Job Scheduling Problem -- 24.4 Genetic Algorithms for Scheduling in Grid Systems -- 24.5 Grid Simulator -- 24.6 Interface for Using a GA-Based Scheduler with the Grid Simulator -- 24.7 Experimental Analysis -- 24.8 Conclusions -- References -- 25 Remote Optimization Service -- 25.1 Introduction -- 25.2 Background and State of the Art -- 25.3 ROS Architecture -- 25.4 Information Exchange in ROS -- 25.5 XML in ROS -- 25.6 Wrappers -- 25.7 Evaluation of ROS -- 25.8 Conclusions -- References -- 26 Remote Services for Advanced Problem Optimization -- 26.1 Introduction -- 26.2 SIRVA -- 26.3 MOSET and TIDESI -- 26.4 ABACUS -- References -- INDEX.
520 $a Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One-covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two-delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.
588 $a Description based on publisher supplied metadata and other sources.
590 $a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650 0 $a Computer science -- Mathematics.;Mathematical optimization.;Problem solving. $3 1000121087
655 4 $a Electronic books. $2 local. $3 172687
700 1 $a Blum, Christian. $3 1000121082
700 1 $a Asasi, Pedro. $3 1000121083
700 1 $a Leon, Coromoto. $3 1000121084
700 1 $a Gomez, Juan Antonio. $3 1000121085
776 0 8 $i Print version: $a Alba, Enrique $t Optimization Techniques for Solving Complex Problems : Optimization Techniques for Solving Complex Problems $d Hoboken : John Wiley & Sons, Incorporated,c2009 $z 9780470293324
797 2 $a ProQuest (Firm)
830 0 $a Wiley Series on Parallel and Distributed Computing Ser. $3 1000121086
856 4 0 $u https://ebookcentral.proquest.com/lib/nkut-ebooks/detail.action?docID=427544 $z Click to View - 點擊此處查看電子書全文