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Evolutionary Optimization Algorithms; Biologically Inspired and Population-Based Approaches to Computer Intelligence

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Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence by Dan Simon provides a comprehensive and in-depth exploration of the theoretical foundations, mathematical principles, and practical implementations of evolutionary optimization algorithms. The book examines computational intelligence paradigms inspired by natural processes and population behaviors, including biological evolution, species migration, bird flocking, and ant colony foraging. Utilizing a systematic, bottom-up approach, Dan Simon bridges the gap between rigorous mathematical theory—such as Markov chain modeling and dynamic systems—and practical code implementation. The text covers a broad spectrum of metaheuristic algorithms, ranging from classical methods (Genetic Algorithms, Genetic Programming, Differential Evolution) to modern population-based and swarm intelligence techniques (Particle Swarm Optimization, Ant Colony Optimization, Biogeography-Based Optimization, and Bacterial Foraging). Additionally, it addresses specialized topics such as multi-objective optimization, constrained optimization, and algorithm tuning.

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Availability

Detail Information

Series Title

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Call Number

006.3 SIM e

Publisher

United states of america : WILEY., 2013

Collation

xxx, 742 Hlm. ; 24 cm.

Language

English

ISBN/ISSN

978-0-470-93741-9

Classification

006.3

Content Type

text

Media Type

unmediated

Carrier Type

volume

Edition

Cetakan pertama, 2013

Subject(s)

Computer Algorithms
Evolutionary Computation

Specific Detail Info

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Statement of Responsibility

Dan Simon

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