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Many aspects of Nature, Biology or even from Society have become part of the techniques and algorithms used in computer science or they have been used to enhance or hybridize several techniques through the inclusion of advanced evolution, cooperation or biologically based additions.The previous NICSO workshops were held in Granada, Spain, 2006, Acireale, Italy, 2007, and in Tenerife, Spain, 2008. As in the previous editions, NICSO 2010, held in Granada, Spain, was conceived as a forum for the latest ideas and the state of the art research related to nature inspired cooperative strategies. The contributions collected in this book cover topics including nature-inspired techniques like Genetic Algorithms, Evolutionary Algorithms, Ant and Bee Colonies, Swarm Intelligence approaches, Neural Networks, several Cooperation Models, Structures and Strategies, Agents Models, Social Interactions, as well as new algorithms based on the behaviour of fireflies or bats.
List of contents
A Metabolic Subsumption Architecture for Cooperative Control of the e-Puck.- Social Target Localization in a Population of Foragers.- Using Knowledge Discovery in Cooperative Strategies: Two Case Studies.- Hybrid Cooperation Models for the Tool Switching Problem.- Fault Diagnosis in Industrial Systems Using Bioinspired Cooperative Strategies.- A New Metaheuristic Bat-Inspired Algorithm.- Evaluation of a Catalytic Search Algorithm.- Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics.- Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization.- CO2RBFN for Short and Medium Term Forecasting of the Extra-Virgin Olive Oil Price.- 3D Cell Pattern Generation in Artificial Development.- Partial Imitation Rule in Iterated Prisoner Dilemma Game on a Square Lattice.- A Dynamical Game Model for Sustainable Development.- Studying the Influence of the Objective Balancing Parameter in the Performance of a Multi-Objective Ant Colony Optimization Algorithm.- HC12: Highly Scalable Optimisation Algorithm.- Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software.- Evolutionary Algorithms for Planar MEMS Design Optimisation: A Comparative Study.- A Distributed Service Oriented Framework for Metaheuristics Using a Public Standard.- Cellular Genetic Algorithm on Graphic Processing Units.- Evolutionary Approaches to Joint Nash - Pareto Equilibria.- Accelerated Genetic Algorithms with Markov Chains.- Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms.- Structural Versus Evaluation Based Solutions Similarity in Genetic Programming Based System Identification.- Artificial Bee Colony Optimization: A New Selection Scheme and Its Performance.- AHeuristic-Based Bee Colony Algorithm for the Multiprocessor Scheduling Problem.- A Bumble Bees Mating Optimization Algorithm for Global Unconstrained Optimization Problems.- A Neural-Endocrine Architecture for Foraging in Swarm Robotic Systems.- Using Entropy for Evaluating Swarm Intelligence Algorithms.- Empirical Study of Performance of Particle Swarm Optimization Algorithms Using Grid Computing.- Using PSO and RST to Predict the Resistant Capacity of Connections in Composite Structures.- Improvement Strategies for Multi-swarm PSO in Dynamic Environments.- Particle Swarm Optimization Based Tuning of Genetic Programming Evolved Classifier Expressions.
Summary
Many aspects of Nature, Biology or even from Society have become part of the techniques and algorithms used in computer science or they have been used to enhance or hybridize several techniques through the inclusion of advanced evolution, cooperation or biologically based additions.
The previous NICSO workshops were held in Granada, Spain, 2006, Acireale, Italy, 2007, and in Tenerife, Spain, 2008. As in the previous editions, NICSO 2010, held in Granada, Spain, was conceived as a forum for the latest ideas and the state of the art research related to nature inspired cooperative strategies. The contributions collected in this book cover topics including nature-inspired techniques like Genetic Algorithms, Evolutionary Algorithms, Ant and Bee Colonies, Swarm Intelligence approaches, Neural Networks, several Cooperation Models, Structures and Strategies, Agents Models, Social Interactions, as well as new algorithms based on the behaviour of fireflies or bats.
