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Swarm Intelligence
Russell C. Eberhart (Author), Yuhui Shi (Author), James Kennedy (Author)
9781558605954, Elsevier Science
Hardback, published 11 April 2001
544 pages
23.5 x 18.7 x 3.1 cm, 1.05 kg
"Well received the September UK Game industry show." --Recent publicity includes a mention in Visual Basic Design Magazine, June issue.
Traditional methods for creating intelligent computational systems have privileged private "internal" cognitive and computational processes. In contrast, Swarm Intelligence argues that human intelligence derives from the interactions of individuals in a social world and further, that this model of intelligence can be effectively applied to artificially intelligent systems. The authors first present the foundations of this new approach through an extensive review of the critical literature in social psychology, cognitive science, and evolutionary computation. They then show in detail how these theories and models apply to a new computational intelligence methodology—particle swarms—which focuses on adaptation as the key behavior of intelligent systems. Drilling down still further, the authors describe the practical benefits of applying particle swarm optimization to a range of engineering problems. Developed by the authors, this algorithm is an extension of cellular automata and provides a powerful optimization, learning, and problem solving method. This important book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation and by applying these insights to the solving of difficult engineering problems. Researchers and graduate students in any of these disciplines will find the material intriguing, provocative, and revealing as will the curious and savvy computing professional.
Introduction
Part 1: Foundations
Life and Intelligence
Optimization by Trial and Error
On our Nonexistence as Entities
Evolutionary Computation Theory and Paradigms
Humans - Actual, Imagined and Implied
Thinking is Social
Part 2: Particle Optimization and Collective Intelligence
The Binary Particle Swarm
Variations and Comparisons;
Applications
Implications and Speculations
Conclusions
Subject Areas: Expert systems / knowledge-based systems [UYQE], Artificial intelligence [UYQ], Algorithms & data structures [UMB], Stochastics [PBWL]
