Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences

CONTENTS

Contributors

Preface

1. Distributed Cellular Automata: Large-Scale Simulation of Natural Phenomena

P. M. A. Sloot, J. A. Kaandorp, A. G. Hoekstra, and B. J. Overeinder

1.1 Introduction

1.2 Background of Cellular Automata Concepts

1.3 Execution Models for Cellular Automata

1.4 Cellular Automata as Models for Fluid Flow

1.5 Selected Applications

1.6 Summary

2. Parallel Implementations of Evolutionary Algorithms

Hartmut Schmeck, Jürgen Branke, and Udo Kohlmorgen

2.1 Introduction

2.2 Standard Approaches to Parallelizing Evolutionary Algorithms

2.3 Parallel Global Selection

2.4 Setup for Experimental Investigation

2.5 Test Problems

2.6 Discussion of Results

2.7 Summary

3. Toward Hybrid Biologically Inspired Heuristics

El-Ghazali Talbi

3.1 Introduction

3.2 Design Issues

3.3 Implementation Issues

3.4 A Grammar for Extended Hybridization Schemes

3.5 Summary

4. Nature-Inspired Optimization Algorithms for Parallel Simulations

Azzedine Boukerche and Sajal K. Das

4.1 Introduction

4.2 What Is Parallel Simulation?

4.3 Previous and Related Work

4.4 Simulated Annealing: Conservative Partitioning

4.5 Genetic Algorithm: Optimistic Load Balancing

4.6 Stochastic Learning Automata

4.7 Summary

5. An Introduction to Genetic-Based Scheduling in Parallel Processor Systems

Albert Y. Zomaya, Richard C. Lee, and Stephan Olariu

5.1 Introduction

5.2 Task Scheduling and Problem Formulation

5.3 The Proposed ...