Handbook of Bioinspired Algorithms and Applications
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Handbook of Bioinspired Algorithms and Applications

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Description

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.show more

Product details

  • Hardback | 704 pages
  • 172.7 x 256.5 x 38.1mm | 1,292.75g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • 274 equations; 1 Halftones, black and white; 104 Tables, black and white; 300 Illustrations, black and white
  • 1584884754
  • 9781584884750
  • 2,476,062

Table of contents

MODELS AND PARADIGMSEvolutionary Algorithms; E. Alba and C. CottaAn Overview of Neural Networks Models; J. Taheri and A.Y. ZomayaAnt Colony Optimization; M. Guntsch and J. BrankeSwarm Intelligence; M. Belal, J. Gaber, H. El-Sayed, and A. AlmojelParallel Genetic Programming: Methodology, History, and Application to Real-Life Problems; F. Fernandez de VegaParallel Cellular Algorithms and Programs; D. TaliaDecentralized Cellular Evolutionary Algorithms; E. Alba, B. Dorronsoro, M. Giacobino, and M. TomassiniOptimization via Gene Expression Algorithms; F. BurkowskiDynamic Updating DNA Computing Algorithms; Z.F. Qiu and M. LuA Unified View on Metaheuristics and Their Hybridization; J. Branke, M. Stein, and H. SchmeckThe Foundations of Autonomic Computing; S. Hariri, B. Khargaria, M. Parashar, and Z. LiAPPLICATION DOMAINSSetting Parameter Values for Parallel Genetic Algorithms: Scheduling Tasks on a Cluster; M. MooreGenetic Algorithms for Scheduling in Grid Computing Environments: A Case Study; K. Crnomarkovic and A.Y. ZomayaMinimization 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. RoyMedical Imaging and Diagnosis Using Genetic Algorithms; U. Maulik, S. Bandyopadhyay, S.K. DasScheduling and Rescheduling with Use of Cellular Automata; F. Seredynski, A. Swiecicka, and A.Y. ZomayaCellular Automata, PDEs, and Pattern Formation; X-S. Yang, Y. YoungAnt Colonies and the Mesh-Partitioning Problem; B. Robic, P. Korosec, and J. SilcSimulating the Strategic Adaptation of Organizations Using OrgSwarm; A. Brabazon, A. Silva, E. Costa, T. Ferra de Sousa, and M. O'NeillBeeHive: New Ideas for Developing Routing Algorithms Inspired by Honey Bee Behavior; H.F. Wedde and M. FarooqSwarming Agents for Decentralized Clustering in Spatial Data; G. Folino, A. Forestiero, and G. SpezzanoBiological Inspired Based Intrusion Detection Models for Mobile Telecommunication Systems; A. Boukerche, K.R.L. Juca, J.B.M. Sobral, and M.S.M.A. NotareSynthesis of Multiple-Valued Circuits by Neural Networks; A. Ngom and I. StojmenovicOn the Computing Capacity of Multiple-Valued Multiple-Threshold Perceptrons; A. Ngom, I. Stojmenovic, and J. ZunicAdvanced Evolutionary Algorithms for Training Neural Networks; E. Alba, J.F. Chicano, F. Luna, G. Luque, and A.J. NebroBio-Inspired Data Mining; T. Sousa, A. Silva, A. Neves, and E. CostaA Hybrid Evolutionary Algorithm for Knowledge Discovery in Microarray Experiments; L. Jourdan, M. Khabzaoui, C. Dhaenens, and E-G. TalbiAn Evolutionary Approach to Problems in Electrical Engineering Design; G. Papa, J. Silc, and B. Korousic-SeljakSolving the Partitioning Problem in Distributed Virtual Environment Systems Using Evolutive Algorithms; P. Morillo, M. Fernandez, and J.M. OrdunaPopulation Learning Algorithm and Its Applications; P. JedrzejowiczBiology-Derived Algorithms in Engineering Optimization; X-S. YangBiomimetic Models for Wireless Sensor Networks; K.H. Jones, K.N. Lodding, S. Olariu, A. Wadaa, L. Wilson, and M. EltoweissyA Cooperative Parallel Metaheuristic Applied to the Graph Coloring Problem; B. Weinberg and E-G. TalbiFrameworks for the Design of Reusable Parallel and Distributed Metaheuristics; N. melba, E-G. Talbi, and S. CahonParallel Hybrid Multiobjective Metaheuristics on P2P Systems; N. Melab, E-G. Talbi, M. Mezmaz, and B. WeiINDEXshow more

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