Applications of Evolutionary Computation: Lecture Notes in Computer Science, cartea 9598
Editat de Giovanni Squillero, Paolo Burellien Limba Engleză Paperback – 3 apr 2016
The57 revised full papers presented together with 17 poster papers were carefullyreviewed and selected from 115 submissions. EvoApplications 2016 consisted ofthe following 13 tracks: EvoBAFIN (natural computing methods in businessanalytics and finance), EvoBIO (evolutionary computation, machine learning anddata mining in computational biology), EvoCOMNET (nature-inspired techniquesfor telecommunication networks and other parallel and distributed systems),EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY(evolutionary computation in energy applications), EvoGAMES (bio-inspiredalgorithms in games), EvoIASP (evolutionary computation in image analysis,signal processing, and pattern recognition), EvoINDUSTRY (nature-inspiredtechniques in industrial settings), EvoNUM (bio-inspired algorithms forcontinuous parameter optimization), EvoPAR (parallel implementation ofevolutionary algorithms), EvoRISK (computational intelligence for riskmanagement, security and defence applications), EvoROBOT (evolutionaryrobotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamicenvironments).
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (2) | 325.03 lei 6-8 săpt. | |
| Springer – 3 apr 2016 | 325.03 lei 6-8 săpt. | |
| Springer – 23 mar 2016 | 653.37 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783319311524
ISBN-10: 3319311522
Pagini: 356
Ilustrații: XXVI, 329 p. 94 illus.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.54 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
ISBN-10: 3319311522
Pagini: 356
Ilustrații: XXVI, 329 p. 94 illus.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.54 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
Cuprins
EvoNUM: Local Fitness Meta-Models with Nearest Neighbor Regression.- Validatingthe Grid Diversity Operator: an Infusion Technique for Diversity Maintenance inPopulation-Based Optimisation Algorithms.- Benchmarking Languages for EvolutionaryAlgorithms.- On the Closest Averaged Hausdorff Archive for a Circularly ConvexPareto Front.- Evolving Smoothing Kernels for Global Optimization.- EvoPAR: ImplementingParallel Differential Evolution on Spark.- ECJ+HADOOP: An Easy Way to Deploy MassiveRuns of Evolutionary Algorithm.- Addressing High Dimensional Multi-Objective OptimizationProblems by Coevolutionary Islands with Overlapping Search Spaces.- CompilablePhenotypes: Accelerating the Evaluation of Individuals in Grammatical Evolution.-GPU Accelerated Molecular Docking Simulation with Genetic Algorithms.- EvoRISK:Challenging Anti-virus Through Evolutionary Malware Obfuscation.- EvoROBOT: LeveragingOnline Racing and Population Cloning in Evolutionary Multirobot Systems.- Multi-AgentBehavior-Based Policy Transfer.- On-line Evolution of Foraging Behaviour in aPopulation of Real Robots.- Hybrid Control for a Real Swarm Robotics System inan Intruder Detection Task.- EvoSTOC: Direct Memory Schemes for Population-BasedIncremental Learning in Cyclically Changing Environments.- Simheuristics forthe Multiobjective Nondeterministic Firefighter Problem in a Time-ConstrainedSetting.- Benchmarking Dynamic Three-Dimensional Bin Packing Problems Using Discrete-EventSimulation.- Genetic Programming Algorithms for Dynamic Environments.- AMemory-Based NSGA-II Algorithm for Dynamic Multi-Objective OptimizationProblems.- Hybrid Dynamic Resampling Algorithms for EvolutionaryMulti-objective Optimization of Invariant-Noise Problems.
Caracteristici
Includes supplementary material: sn.pub/extras