Handbook of Bioinspired Algorithms and Applications
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Summary
The mystique of biologically inspired (or bioinspired) paradigms is their ability to describe and solve complex relationships from intrinsically very simple initial conditions and with little or no knowledge of the search space. Edited by two prominent, well-respected researchers, the Handbook of Bioinspired Algorithms and Applications reveals the connections between bioinspired techniques and the development of solutions to problems that arise in diverse problem domains.
A repository of the theory and fundamentals as well as a manual for practical implementation, this authoritative handbook provides broad coverage in a single source along with numerous references to the available literature for more in-depth information. The book's two sections serve to balance coverage of theory and practical applications. The first section explains the fundamentals of techniques, such as evolutionary algorithms, swarm intelligence, cellular automata, and others. Detailed examples and case studies in the second section illustrate how to apply the theory in actually developing solutions to a particular problem based on a bioinspired technique.
Emphasizing the importance of understanding and harnessing the robust capabilities of bioinspired techniques for solving computationally intractable optimizations and decision-making applications, the Handbook of Bioinspired Algorithms and Applications is an absolute must-read for anyone who is serious about advancing the next generation of computing.
Table of Contents
MODELS AND PARADIGMS
Evolutionary Algorithms; E. Alba and C. Cotta
An Overview of Neural Networks Models; J. Taheri and A.Y. Zomaya
Ant Colony Optimization; M. Guntsch and J. Branke
Swarm Intelligence; M. Belal, J. Gaber, H. El-Sayed, and A. Almojel
Parallel Genetic Programming: Methodology, History, and Application to Real-Life Problems; F. Fernández de Vega
Parallel Cellular Algorithms and Programs; D. Talia
Decentralized Cellular Evolutionary Algorithms; E. Alba, B. Dorronsoro, M. Giacobino, and M. Tomassini
Optimization via Gene Expression Algorithms; F. Burkowski
Dynamic Updating DNA Computing Algorithms; Z.F. Qiu and M. Lu
A Unified View on Metaheuristics and Their Hybridization; J. Branke, M. Stein, and H. Schmeck
The Foundations of Autonomic Computing; S. Hariri, B. Khargaria, M. Parashar, and Z. Li
APPLICATION DOMAINS
Setting Parameter Values for Parallel Genetic Algorithms: Scheduling Tasks on a Cluster; M. Moore
Genetic Algorithms for Scheduling in Grid Computing Environments: A Case Study; K. Crnomarkovic and A.Y. Zomaya
Minimization of SADMs in Unidirectional SONET/WDM Rings Using Genetic Algorithms; A. Mukhopadhyay, U. Biswas, M.K. Naskar, U. Maulik, and S. Bandyopadhyay,
Solving Optimization Problems in Wireless Networks Using Genetic Algorithms; S.K. Das, N. Banerjee, and A. Roy
Medical Imaging and Diagnosis Using Genetic Algorithms; U. Maulik, S. Bandyopadhyay, S.K. Das
Scheduling and Rescheduling with Use of Cellular Automata; F. Seredynski, A. Swiecicka, and A.Y. Zomaya
Cellular Automata, PDEs, and Pattern Formation; X-S. Yang, Y. Young
Ant Colonies and the Mesh-Partitioning Problem; B. Robic, P. Korošec, and J. Šilc
Simulating the Strategic Adaptation of Organizations Using OrgSwarm; A. Brabazon, A. Silva, E. Costa, T. Ferra de Sousa, and M. O'Neill
BeeHive: New Ideas for Developing Routing Algorithms Inspired by Honey Bee Behavior; H.F. Wedde and M. Farooq
Swarming Agents for Decentralized Clustering in Spatial Data; G. Folino, A. Forestiero, and G. Spezzano
Biological Inspired Based Intrusion Detection Models for Mobile Telecommunication Systems; A. Boukerche, K.R.L. Jucá, J.B.M. Sobral, and M.S.M.A. Notare
Synthesis of Multiple-Valued Circuits by Neural Networks; A. Ngom and I. Stojmenovic
On the Computing Capacity of Multiple-Valued Multiple-Threshold Perceptrons; A. Ngom, I. Stojmenovic, and J. Žunic
Advanced Evolutionary Algorithms for Training Neural Networks; E. Alba, J.F. Chicano, F. Luna, G. Luque, and A.J. Nebro
Bio-Inspired Data Mining; T. Sousa, A. Silva, A. Neves, and E. Costa
A Hybrid Evolutionary Algorithm for Knowledge Discovery in Microarray Experiments; L. Jourdan, M. Khabzaoui, C. Dhaenens, and E-G. Talbi
An Evolutionary Approach to Problems in Electrical Engineering Design; G. Papa, J. Šilc, and B. Koroušic-Seljak
Solving the Partitioning Problem in Distributed Virtual Environment Systems Using Evolutive Algorithms; P. Morillo, M. Fernandez, and J.M. Orduña
Population Learning Algorithm and Its Applications; P. Jedrzejowicz
Biology-Derived Algorithms in Engineering Optimization; X-S. Yang
Biomimetic Models for Wireless Sensor Networks; K.H. Jones, K.N. Lodding, S. Olariu, A. Wadaa, L. Wilson, and M. Eltoweissy
A Cooperative Parallel Metaheuristic Applied to the Graph Coloring Problem; B. Weinberg and E-G. Talbi
Frameworks for the Design of Reusable Parallel and Distributed Metaheuristics; N. melba, E-G. Talbi, and S. Cahon
Parallel Hybrid Multiobjective Metaheuristics on P2P Systems; N. Melab, E-G. Talbi, M. Mezmaz, and B. Wei
INDEX
Editorial Reviews
"References are ample and pertinent offering the reader further sources to search. Moreover, both editors and all contributors are experts in the subjects they deal with. It is, no doubt, a necessary repository piece of reference material in technical libraries, computer labs and for any group devoted to the development of optimal algorithms. …This book adds new paths to follow, new practical applications and an enormous amount of imagination."
-BioMedical Engineering OnLine
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