Studies in Computational Intelligence Ser.: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) by Fernando E. B. Otero (2013, Hardcover)

Springer International Publishing A&G

3319016911

9783319016917

168483319

Fernando E. B. Otero

Nature Inspired Cooperative Strategies for Optimization (Nicso 2013)

Hardcover

English

2013

Studies in Computational Intelligence Ser.

Textbook

Xiii, 355 Pages

9.3in

6.1in

239.9 Oz

512

1 Vol.

Q342

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.

2014

Engineering (General), Intelligence (Ai) & Semantics, Data Processing

004

Scholarly & Professional

23

Yes

Computers, Technology & Engineering