Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
* 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
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).
FIKRET ERCAL, PhD, is a professor at Intelligent Systems Center, University of Missouri-Rolla.
STEPHAN OLARIU, PhD, is a professor at Old Dominion University, Norfolk, Virginia.
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