∗ 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).