Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Krasnogor, Natalio (Επιμελητής έκδοσης), Melián-Batista, María Belén (Επιμελητής έκδοσης), Pérez, José Andrés Moreno (Επιμελητής έκδοσης), Moreno-Vega, J. Marcos (Επιμελητής έκδοσης), Pelta, David Alejandro (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Σειρά: Studies in Computational Intelligence, 236
• Θέματα: Engineering.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Engineering design.; Robotics.; Automation.; Engineering Design.; Artificial Intelligence (incl. Robotics).; Robotics and Automation.; Appl.Mathematics/Computational Methods of Engineering.

 The inspiration from Biology and the Natural Evolution process has become a research area within computer science. For instance, the description of the artificial neuron given by McCulloch and Pitts was inspired from biological observations of neural mechanisms; the power of evolution in nature in the diverse species that make up our world has been related to a particular form of problem solving based on the idea of survival of the fittest; similarly, artificial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The first and second editions of the International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), were held in Granada, Spain, 2006, and in Acireale, Italy, 2007, respectively. As in these two previous editions, the aim of NICSO 2008, held in Tenerife, Spain, was to provide a forum were the latest ideas and state of the art research related to nature inspired cooperative strategies for problem solving were 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 nature-inspired techniques like Genetic Algorithms, Ant Colonies, Amorphous Computing, Artificial Immune Systems, Evolutionary Robotics, Evolvable Systems, Membrane Computing, Quantum Computing, Software Self Assembly, Swarm Intelligence, etc. 

 Exploration in Stochastic Algorithms: An Application on – Ant System -- Sensitive Ants: Inducing Diversity in the Colony -- Decentralised Communication and Connectivity in Ant Trail Networks -- Detection of Non-structured Roads Using Visible and Infrared Images and an Ant Colony Optimization Algorithm -- A Nature Inspired Approach for the Uncapacitated Plant Cycle Location Problem -- Particle Swarm Topologies for Resource Constrained Project Scheduling -- Discrete Particle Swarm Optimization Algorithm for Data Clustering -- A Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments -- Exploring Feasible and Infeasible Regions in the Vehicle Routing Problem with Time Windows Using a Multi-objective Particle Swarm Optimization Approach -- Two-Swarm PSO for Competitive Location Problems -- Aerodynamic Wing Optimisation Using SOMA Evolutionary Algorithm -- Experimental Analysis of a Variable Size Mono-population Cooperative-Coevolution Strategy -- Genetic Algorithm for Tardiness Minimization in Flowshop with Blocking -- Landscape Mapping by Multi-population Genetic Algorithm -- An Interactive Simulated Annealing Multi-agents Platform to Solve Hierarchical Scheduling Problems with Goals -- Genetic Algorithm and Advanced Tournament Selection Concept -- Terrain-Based Memetic Algorithms for Vector Quantizer Design -- Cooperating Classifiers -- Evolutionary Multimodal Optimization for Nash Equilibria Detection -- On the Computational Properties of the Multi-Objective Neural Estimation of Distribution Algorithm -- Optimal Time Delay in the Control of Epidemic -- Parallel Hypervolume-Guided Hyperheuristic for Adapting the Multi-objective Evolutionary Island Model -- A Cooperative Strategy for Guiding the Corridor Method -- On the Performance of Homogeneous and Heterogeneous Cooperative Search Strategies.