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Foundations of Computational Intelligence Volume 3: Global Optimization

ISBN-13: 9783642010842 / Angielski / Twarda / 2009 / 528 str.

Ajith Abraham;Aboul Ella Hassanien;Patrick Siarry
Foundations of Computational Intelligence Volume 3: Global Optimization Ajith Abraham, Aboul Ella Hassanien, Patrick Siarry, Andries Engelbrecht 9783642010842 Springer-Verlag Berlin and Heidelberg GmbH &  - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Foundations of Computational Intelligence Volume 3: Global Optimization

ISBN-13: 9783642010842 / Angielski / Twarda / 2009 / 528 str.

Ajith Abraham;Aboul Ella Hassanien;Patrick Siarry
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Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc. Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems. This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

Kategorie:
Informatyka, Bazy danych
Kategorie BISAC:
Computers > Artificial Intelligence - General
Technology & Engineering > Engineering (General)
Wydawca:
Springer-Verlag Berlin and Heidelberg GmbH &
Seria wydawnicza:
Studies in Computational Intelligence
Język:
Angielski
ISBN-13:
9783642010842
Rok wydania:
2009
Dostępne języki:
Angielski
Wydanie:
2009
Numer serii:
000318395
Ilość stron:
528
Waga:
2.05 kg
Wymiary:
23.523.5 x 15.5
Oprawa:
Twarda
Wolumenów:
01
Dodatkowe informacje:
Wydanie ilustrowane

Global Optimization Algorithms: Theoretical Foundations and Perspectives.- Genetic Algorithms for the Use in Combinatorial Problems.- Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications.- Global Optimization Using Harmony Search: Theoretical Foundations and Applications.- Hybrid GRASP Heuristics.- Particle Swarm Optimization: Performance Tuning and Empirical Analysis.- Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study.- Reformulations in Mathematical Programming: A Computational Approach.- Graph-Based Local Elimination Algorithms in Discrete Optimization.- Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems.- Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning.- Global Optimization Algorithms: Applications.- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy.- Evolutionary Computing in Statistical Data Analysis.- Meta-heuristics for System Design Engineering.- Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms.- Multi-objective Team Forming Optimization for Integrated Product Development Projects.- Genetic Algorithms for Task Scheduling Problem.- PSO_Bounds: A New Hybridization Technique of PSO and EDAs.

Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University. Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.

This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.



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