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Engineering - Computational Intelligence and Complexity | Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) - Learning, Optimization

Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

Learning, Optimization and Interdisciplinary Applications

Terrazas, German, Otero, Fernando E. B., Masegosa, Antonio D. (Eds.)

2014, XIV, 355 p. 82 illus., 12 illus. in color.

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  • Recent research on Nature Inspired Cooperative Strategies for Optimizationcrest123
  • Edited outcome of the sixth International Workshop NICSO 2013 Nature Inspired Cooperative Strategies for Optimization held September 2nd - 4th, 2013 at Canterbury, UK
  • Written by leading experts in the field

Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation.

This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm Intelligence, Hyper-heuristics, Evolutionary Algorithms, Cellular Automata, Artificial Bee Colony, Dynamic Optimization, Support Vector Machines, Multi-Agent Systems, Ant Clustering, Evolutionary Design Optimisation, Game Theory and other several Cooperation Models.

Content Level » Research

Keywords » Computational Intelligence - Nature Inspired Cooperative Strategies for Optimization

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

Extending the ABC-Miner Bayesian Classification Algorithm.- A Multiple Pheromone Ant Clustering Algorithm.- An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem.- Using a Scouting Predator-Prey Optimizer to Train Support Vector Machines with non PSD Kernels.- Response Surfaces with Discounted Information for Global Optima Tracking in Dynamic Environments.- Fitness based Self Adaptive Differential.- Adaptation schemes and dynamic optimization problems: a basic study on the Adaptive Hill Climbing Memetic Algorithm.- Using base position errors in an entropy-based evaluation function for the study of genetic code adaptability.- An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems.- Corner Based Many-Objective Optimization.- Escaping Local Optima via Parallelization and.- An Improved Genetic Based Keyword Extraction Technique.- Part-of-Speech Tagging Using Evolutionary Computation.- A Cooperative approach using ants and bees for the graph coloring problem.- Artificial Bee Colony Training of Neural Networks.- Nonlinar optimization in landscapes with planar regions.- Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm.- Meta Morphic Particle Swarm Optimization.- Empirical study of computational intelligence strategies for biochemical systems modelling.- Metachronal waves in Cellular Automata: Cilia-like manipulation in actuator arrays.- Team of A-Teams Approach for Vehicle Routing Problem with Time Windows.- Self-adaptable Group Formation of Reconfigurable Agents in Dynamic Environments.- A Choice Function Hyper-Heuristic for the Winner Determination Problem.- Automatic Generation of Heuristics for Constraint Satisfaction Problems.- Branching Schemes and Variable Ordering Heuristics for Constraint Satisfaction Problems: Is there Something to Learn.- Nash Equilibria Detection for Discrete-time Generalized Cournot Dynamic Oligopolies.

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