Nature Inspired Cooperative Strategies for Optimization (NICSO 2007)Natalio Krasnogor, Vincenzo Nicosia, Mario Pavone, David Alejandro Pelta Biological and natural processes have been a continuous source of inspiration for the sciences and engineering. For instance, the work of Wiener in cybernetics was influenced by feedback control processes observable in biological systems; McCulloch and Pitts description of the artificial neuron was instigated by biological observations of neural mechanisms; the idea of survival of the fittest inspired the field of evolutionary algorithms and similarly, artificial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The second International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), was held in Acireale, Italy, during November 8-10, 2007. The aim for NICSO 2007 was to provide a forum were the latest ideas and state of the art research related to cooperative strategies for problem solving arising from Nature could be discussed. The contributions collected in this book were strictly peer reviewed by at least three members of the international programme committee, to whom we are indebted for their support and assistance. The topics covered by the contributions include several well established nature inspired techniques like Genetic Algorithms, Ant Colonies, Artificial Immune Systems, Evolutionary Robotics, Evolvable Systems, Membrane Computing, Quantum Computing, Software Self Assembly, Swarm Intelligence, etc. |
Contents
Optimization | 14 |
Introduction | 15 |
Flockingbased Document Clustering on the Graphics Processing Unit | 27 |
Artificial Immune System for Collaborative Spam Filtering | 39 |
MP Systems and Hybrid Petri Nets 53 | 52 |
Spatial Sorting of Binary Metadata Documents via NatureInspired | 63 |
hCHAC4 an ACO Algorithm for Solving the FourCriteria Military | 73 |
E MezuraMontes | 84 |
A New Nature Inspired Computational | 221 |
Social Impact based Approach to Feature Subset Selection Martin Macaš Lenka Lhotska and Vaclav Kremen | 239 |
Influence of Different Deviations Allowed for Equality Constraints | 249 |
Learning Classifier System with Selfadaptive Discovery Mechanism | 273 |
Learning Robust Dynamic Networks in Prokaryotes | 299 |
Discrete Particle Swarm Optimization for the Minimum Labelling | 312 |
A Surface Tension and Coalescence Model for Dynamic Distributed | 335 |
A Hybrid Genetic Algorithm for the Travelling Salesman Problem | 357 |
Searching Ground States of Ising Spin Glasses with Genetic Algorithms | 85 |
Andrei Bautu and Elena Bautu | 95 |
A New EvolutionarySwarm Cooperative Algorithm | 105 |
An Adaptive Metaheuristic for the Simultaneous Resolution | 125 |
Honey Bees Mating Optimization Algorithm for the Vehicle | 138 |
Dynamic Adaptation of Genetic Operators Probabilities | 159 |
Comparing the Neural and Immune | 179 |
Memetic Algorithm for the Generalized Asymmetric Traveling Salesman | 199 |
Gemma BelEnguix and M Dolores JimenezLopez | 379 |
Implementation of Massive Parallel Networks of Evolutionary | 399 |
A Genetic Algorithm Framework Applied to Quantum | 419 |
Thomas Sierocinski Antony Le Bechec Nathalie Theret and Dimitri Petritis | 443 |
Automatic Selection for the Beta Basis Function Neural Networks Habib Dhahri and Adel Alimi | 461 |
A Problem of Generalization Which Works | 475 |
A Genetic Algorithm Based on Complex Networks Theory | 495 |