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Other titles in the Operations Research/Computer Science Interfaces series:

Operations Research/Computer Science Interfaces #42: Cellular Genetic Algorithms

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Operations Research/Computer Science Interfaces #42: Cellular Genetic Algorithms Cover

 

Synopses & Reviews

Publisher Comments:

CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability. The methods are benchmarked against well-known metaheutistics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.

Synopsis:

Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book with equal and parallel emphasis on both theory and practice. This book is a key source for studying and designing cellular GAs, as well as a self-contained primary reference book for these algorithms.

Table of Contents

Introduction to cellular GAs.- Panmictic GAs: cGAs versus steady-state and generational GAs.- Structured GAs: cGAs versus distributed GAs.- Using the ratio concept to define efficient cGAs.- New models with dynamic population shape in cGAs.- Multi-objective cGAs.- Memetic cGAs.- Theory of convergence for cGAs.- cGAs versus other metaheuristics.- cGAs for discrete optimization.- cGAs for continuous optimization.- cGAs for the vehicle routing problem.- cGAs for MANETS (mobile ad-hoc networks).- Index.

Product Details

ISBN:
9780387776095
Author:
Alba, Enrique
Publisher:
Springer
Author:
Dorronsoro, Bernab??
Author:
Dorronsoro, Bernabe
Location:
Boston, MA
Subject:
Operations Research
Subject:
Applied
Subject:
Linear Programming
Subject:
Programming - Algorithms
Subject:
Operations Research/Decision Theory
Subject:
Genetics and Population Dynamics
Subject:
Algorithms
Subject:
Production/Logistics/Supply Chain Management
Subject:
OPTIMIZATION
Subject:
Mathematical Modeling and Industrial Mathematics
Subject:
Software Engineering-Algorithms
Subject:
Operation Research/Decision Theory
Subject:
Economics
Subject:
B
Subject:
Business and Economics
Subject:
Genetics_xMathematics
Subject:
Mathematical optimization
Copyright:
Edition Description:
Book
Series:
Operations Research Computer Science Interfaces Series
Series Volume:
42
Publication Date:
20080606
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
262
Dimensions:
235 x 155 mm

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Operations Research/Computer Science Interfaces #42: Cellular Genetic Algorithms New Hardcover
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Product details 262 pages Springer - English 9780387776095 Reviews:
"Synopsis" by , Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book with equal and parallel emphasis on both theory and practice. This book is a key source for studying and designing cellular GAs, as well as a self-contained primary reference book for these algorithms.
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