Picture 1 of 4
Stock photo
Picture 1 of 4
Stock photo
Intelligent Optimisation Techniques : Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks by D. T. Pham and D. Karaboga (2011, Trade Paperback)
F
Fundamentally Physics (1055)
100% positive feedback
Price:
US $70.85
(inclusive of GST)
ApproximatelyS$ 91.02
+ $49.38 shipping
Returns:
30 days return. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.
Condition:
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
About this product
Product Identifiers
PublisherSpringer London, The Limited
ISBN-101447111869
ISBN-139781447111863
eBay Product ID (ePID)143683028
Product Key Features
Number of PagesX, 302 Pages
LanguageEnglish
Publication NameIntelligent Optimisation Techniques : Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Publication Year2011
SubjectProgramming / Algorithms, Engineering (General), Intelligence (Ai) & Semantics, Computer Simulation, Industrial Design / General, Neural Networks, Cad-Cam
TypeTextbook
AuthorD. T. Pham, D. Karaboga
Subject AreaComputers, Technology & Engineering
FormatTrade Paperback
Dimensions
Item Height0.3 in
Item Weight17 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
Number of Volumes1 vol.
IllustratedYes
Table Of Content1 Introduction.- 1.1 Genetic Algorithms.- 1.2 Tabu Search.- 1.3 Simulated Annealing.- 1.4 Neural Networks.- 1.5 Performance of Different Optimisation Techniques on Benchmark Test Functions.- 1.6 Performance of Different Optimisation Techniques on Travelling Salesman Problem.- 1.7 Summary.- 2 Genetic Algorithms.- 2.1 New Models.- 2.2 Engineering Applications.- 2.3 Summary.- 3 Tabu Search.- 3.1 Optimising the Effective Side-Length Expression for the Resonant Frequency of a Triangular Microstrip Antenna.- 3.2 Obtaining a Simple Formula for the Radiation Efficiency of a Resonant Rectangular Microstrip Antenna.- 3.3 Training Recurrent Neural Networks for System Identification.- 3.4 Designing Digital Finite-Impulse-Response Filters.- 3.5 Tuning PID Controller Parameters.- 4 Simulated Annealing.- 4.1 Optimal Alignment of Laser Chip and Optical Fibre.- 4.2 Inspection Stations Allocation and Sequencing.- 4.3 Economic Lot-Size Production.- 4.4 Summary.- 5 Neural Networks.- 5.1 VLSI Placement using MHSO Networks.- 5.2 Satellite Broadcast Scheduling using a Hopfield Network.- 5.3 Summary.- Appendix 1 Classical Optimisation.- A1.1 Basic Definitions.- A1.2 Classification of Problems.- A1.3 Classification of Optimisation Techniques.- References.- Appendix 2 Fuzzy Logic Control.- A2.1 Fuzzy Sets.- A2.1.1 Fuzzy Set Theory.- A2.1.2 Basic Operations on Fuzzy Sets.- A2.2 Fuzzy Relations.- A2.3 Compositional Rule of Inference.- A2.4 Basic Structure of a Fuzzy Logic Controller.- A2.5 Studies in Fuzzy Logic Control.- References.- Appendix 3 Genetic Algorithm Program.- Appendix 4 Tabu Search Program.- Appendix 5 Simulated Annealing Program.- Appendix 6 Neural Network Programs.- Author Index.
SynopsisThis book covers four optimisation techniques loosely classified as "intelligent": genetic algorithms, tabu search, simulated annealing and neural networks. - Genetic algorithms (GAs) locate optima using processes similar to those in natural selection and genetics. - Tabu search is a heuristic procedure that employs dynamically generated constraints or tabus to guide the search for optimum solutions. - Simulated annealing finds optima in a way analogous to the reaching of minimum energy configurations in metal annealing. - Neural networks are computational models of the brain. Certain types of neural networks can be used for optimisation by exploiting their inherent ability to evolve in the direction of the negative gradient of an energy function and to reach a stable minimum of that function. Aimed at engineers, the book gives a concise introduction to the four techniques and presents a range of applications drawn from electrical, electronic, manufacturing, mechanical and systems engineering. The book contains listings of C programs implementing the main techniques described to assist readers wishing to experiment with them. The book does not assume a previous background in intelligent optl1TIlsation techniques. For readers unfamiliar with those techniques, Chapter 1 outlines the key concepts underpinning them. To provide a common framework for comparing the different techniques, the chapter describes their performances on simple benchmark numerical and combinatorial problems. More complex engineering applications are covered in the remaining four chapters of the book., This book covers four optimisation techniques loosely classified as "intelligent" genetic algorithms, tabu search, simulated annealing and neural networks. - Genetic algorithms (GAs) locate optima using processes similar to those in natural selection and genetics. - Tabu search is a heuristic procedure that employs dynamically generated constraints or tabus to guide the search for optimum solutions. - Simulated annealing finds optima in a way analogous to the reaching of minimum energy configurations in metal annealing. - Neural networks are computational models of the brain. Certain types of neural networks can be used for optimisation by exploiting their inherent ability to evolve in the direction of the negative gradient of an energy function and to reach a stable minimum of that function. Aimed at engineers, the book gives a concise introduction to the four techniques and presents a range of applications drawn from electrical, electronic, manufacturing, mechanical and systems engineering. The book contains listings of C programs implementing the main techniques described to assist readers wishing to experiment with them. The book does not assume a previous background in intelligent optl1TIlsation techniques. For readers unfamiliar with those techniques, Chapter 1 outlines the key concepts underpinning them. To provide a common framework for comparing the different techniques, the chapter describes their performances on simple benchmark numerical and combinatorial problems. More complex engineering applications are covered in the remaining four chapters of the book., This book covers four optimisation techniques loosely classified as "intelligent": genetic algorithms, tabu search, simulated annealing and neural networks. * Genetic algorithms (GAs) locate optima using processes similar to those in natural selection and genetics. * Tabu search is a heuristic procedure that employs dynamically generated constraints or tabus to guide the search for optimum solutions. * Simulated annealing finds optima in a way analogous to the reaching of minimum energy configurations in metal annealing. * Neural networks are computational models of the brain. Certain types of neural networks can be used for optimisation by exploiting their inherent ability to evolve in the direction of the negative gradient of an energy function and to reach a stable minimum of that function. Aimed at engineers, the book gives a concise introduction to the four techniques and presents a range of applications drawn from electrical, electronic, manufacturing, mechanical and systems engineering. The book contains listings of C programs implementing the main techniques described to assist readers wishing to experiment with them. The book does not assume a previous background in intelligent optl1TIlsation techniques. For readers unfamiliar with those techniques, Chapter 1 outlines the key concepts underpinning them. To provide a common framework for comparing the different techniques, the chapter describes their performances on simple benchmark numerical and combinatorial problems. More complex engineering applications are covered in the remaining four chapters of the book.
LC Classification NumberQ334-342
Be the first to write a review
