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Solutions to Parallel and Distributed Computing Problems. Lessons from Biological Sciences. Edition No. 1. Wiley Series on Parallel and Distributed Computing

  • ID: 2244893
  • Book
  • November 2000
  • 308 Pages
  • John Wiley and Sons Ltd
Solving problems in parallel and distributed computing through the use of bio-inspired techniques. Recent years have seen a surge of interest in computational methods patterned after natural phenomena, with biologically inspired techniques such as fuzzy logic, neural networks, simulated annealing, genetic algorithms, or evolutionary computer models increasingly being harnessed for problem solving in parallel and distributed computing. Solutions to Parallel and Distributed Computing Problems presents a comprehensive review of the state of the art in the field, providing researchers and practitioners with critical information on the use of bio-inspired techniques for improving software and hardware design in high-performance computing. Through contributions from top leaders in the field, this important book brings together current research results, exploring some of the most intriguing and cutting-edge topics from the world of biocomputing, including:

Parallel and distributed computing of cellular automata and evolutionary algorithms

How the speedup of bio-inspired algorithms will help their applicability in a wide range of problems

Solving problems in parallel simulation through such techniques as simulated annealing algorithms and genetic algorithms

Techniques for solving scheduling and load-balancing problems in parallel and distributed computers

Applying neural networks for problem solving in wireless communication systems
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Distributed Cellular Automata: Large-Scale Simulation of Natural Phenomena (P. Sloot, et al.).

Parallel Implementations of Evolutionary Algorithms (H. Schmeck, et al.).

Toward Hybrid Biologically Inspired Heuristics (E.-G. Talbi).

Nature-Inspired Optimization Algorithms for Parallel Simulations (A. Boukerche & S. Das).

An Introduction to Genetic-Based Scheduling in Parallel Processor Systems (A. Zomaya, et al.).

Mapping Tasks onto Distributed Heterogeneous Computing Systems Using a Genetic Algorithm Approach (M. Theys, et al.).

Evolving Cellular Automata-Based Algorithms for Multiprocessor Scheduling (F. Seredynski).

Parallel Task Mapping with Biological Computing Models (T. El-Ghazawi, et al.).

Scheduling Parallel Programs Using Genetic Algorithms (I. Ahmad, et al.).

Applications of Neural Networks to Mobile Communication Systems (A. Boukerche & M. Notare).

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Albert Y. Zomaya The University of Western Australia.

Fikret Ercal University of MissouriÂRolla.

Stephan Olariu Old Dominion University.
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