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Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

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Cover of 'Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)'

Table of Contents

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    Book Overview
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    Chapter 1 Extending the ABC-Miner Bayesian Classification Algorithm
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    Chapter 2 A Multiple Pheromone Ant Clustering Algorithm
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    Chapter 3 An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem
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    Chapter 4 Using a Scouting Predator-Prey Optimizer to Train Support Vector Machines with non PSD Kernels
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    Chapter 5 Response Surfaces with Discounted Information for Global Optima Tracking in Dynamic Environments
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    Chapter 6 Fitness Based Self Adaptive Differential Evolution
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    Chapter 7 Adaptation Schemes and Dynamic Optimization Problems: A Basic Study on the Adaptive Hill Climbing Memetic Algorithm
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    Chapter 8 Using Base Position Errors in an Entropy-Based Evaluation Function for the Study of Genetic Code Adaptability
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    Chapter 9 An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems
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    Chapter 10 Corner Based Many-Objective Optimization
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    Chapter 11 Escaping Local Optima via Parallelization and Migration
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    Chapter 12 An Improved Genetic Based Keyword Extraction Technique
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    Chapter 13 Part-of-Speech Tagging Using Evolutionary Computation
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    Chapter 14 A Cooperative Approach Using Ants and Bees for the Graph Coloring Problem
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    Chapter 15 Artificial Bee Colony Training of Neural Networks
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    Chapter 16 Nonlinear Optimization in Landscapes with Planar Regions
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    Chapter 17 Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm
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    Chapter 18 Meta Morphic Particle Swarm Optimization
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    Chapter 19 Empirical Study of Computational Intelligence Strategies for Biochemical Systems Modelling
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    Chapter 20 Metachronal Waves in Cellular Automata: Cilia-Like Manipulation in Actuator Arrays
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    Chapter 21 Team of A-Teams Approach for Vehicle Routing Problem with Time Windows
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    Chapter 22 Self-adaptable Group Formation of Reconfigurable Agents in Dynamic Environments
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    Chapter 23 A Choice Function Hyper-heuristic for the Winner Determination Problem
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    Chapter 24 Automatic Generation of Heuristics for Constraint Satisfaction Problems
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    Chapter 25 Branching Schemes and Variable Ordering Heuristics for Constraint Satisfaction Problems: Is There Something to Learn?
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    Chapter 26 Nash Equilibria Detection for Discrete-Time Generalized Cournot Dynamic Oligopolies
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Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)
Published by
Springer, Cham, January 2014
DOI 10.1007/978-3-319-01692-4
978-3-31-901691-7, 978-3-31-901692-4

German Terrazas, Fernando E. B. Otero, Antonio D. Masegosa

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 21%
Student > Master 4 9%
Lecturer 3 6%
Student > Doctoral Student 2 4%
Researcher 2 4%
Other 5 11%
Unknown 21 45%
Readers by discipline Count As %
Computer Science 12 26%
Engineering 4 9%
Physics and Astronomy 2 4%
Mathematics 2 4%
Chemical Engineering 2 4%
Other 3 6%
Unknown 22 47%