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Optimization techniques for solving complex problems / edited by Enrique Alba, Christian Blum, Pedro Isasi [and 2 others].

Contributor(s): Material type: TextTextPublication details: Hoboken, New Jersey : Wiley, c2009Description: xxi, 476 pages : illustrations ; 24 cmISBN:
  • 9780470293324
Subject(s): LOC classification:
  • QA 76.9 .O68 2009
Contents:
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; 2 Neural Lazy Local Learning; 3 Optimization Using Genetic Algorithms with Micropopulations; 4 Analyzing Parallel Cellular Genetic Algorithms; 5 Evaluating New Advanced Multiobjective Metaheuristics; 6 Canonical Metaheuristics for Dynamic Optimization Problems; 7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms.
Summary: Solving Complex Problems addresses real problems and the modern optimization techniques used to solve them. Thorough examples illustrate the applications themselves, as well as the actual performance of the algorithms. Application areas include computer science, engineering, transportation, telecommunications, and bioinformatics, making the book especially useful to practitioners in those areas.
Item type: Books
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Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Civil Engineering GC QA 76.9 .O68 2009 (Browse shelf(Opens below)) c.1 Available NULIB000014390

Includes bibliographical references and index.

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; 2 Neural Lazy Local Learning; 3 Optimization Using Genetic Algorithms with Micropopulations; 4 Analyzing Parallel Cellular Genetic Algorithms; 5 Evaluating New Advanced Multiobjective Metaheuristics; 6 Canonical Metaheuristics for Dynamic Optimization Problems; 7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms.

Solving Complex Problems addresses real problems and the modern optimization techniques used to solve them. Thorough examples illustrate the applications themselves, as well as the actual performance of the algorithms. Application areas include computer science, engineering, transportation, telecommunications, and bioinformatics, making the book especially useful to practitioners in those areas.

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