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220822 ||| eng |
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|a 9783036516707
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|a books978-3-0365-1670-7
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|a 9783036516691
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| 100 |
1 |
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|a Quiroz, Marcela
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| 245 |
0 |
0 |
|a Numerical and Evolutionary Optimization 2020
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (364 p.)
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| 653 |
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|a robust optimization
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| 653 |
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|a computational fluid dynamics
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| 653 |
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|a fully linear models
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| 653 |
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|a continuation
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| 653 |
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|a radial basis functions
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| 653 |
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|a trapezoidal fuzzy numbers
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| 653 |
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|a Metropolis
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| 653 |
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|a pipe breaking
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| 653 |
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|a kriging method
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| 653 |
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|a pseudo random number generator
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| 653 |
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|a forecasting
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| 653 |
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|a decision maker profile
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| 653 |
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|a Mathematics & science / bicssc
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| 653 |
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|a JSSP
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| 653 |
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|a Hybrid Simulated Annealing
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| 653 |
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|a trust region methods
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| 653 |
|
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|a cognitive tasks
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| 653 |
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|a Convolutional Neural Network
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| 653 |
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|a multiobjective optimization
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| 653 |
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|a deep learning
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| 653 |
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|a usability evaluation
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| 653 |
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|a hybrid evolutionary approach
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| 653 |
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|a Pareto Tracer
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| 653 |
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|a surrogate modeling
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| 653 |
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|a project portfolio selection problem
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| 653 |
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|a LSTM
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| 653 |
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|a multi-objective evolutionary optimization
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| 653 |
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|a protein structure prediction
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| 653 |
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|a ROOT
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| 653 |
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|a drainage rehabilitation
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| 653 |
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|a linear programming
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| 653 |
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|a multi-objective portfolio optimization problem
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| 653 |
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|a CMOSA
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| 653 |
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|a PVT variations
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| 653 |
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|a FP16
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| 653 |
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|a COVID-19
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| 653 |
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|a outranking relationships
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| 653 |
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|a multi-objective optimization
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| 653 |
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|a CMOS differential pair
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| 653 |
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|a density estimators
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| 653 |
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|a structural biology
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| 653 |
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|a evolutionary algorithms
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| 653 |
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|a steady state algorithms
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| 653 |
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|a differential evolution
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| 653 |
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|a CMOTA
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| 653 |
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|a profile assessment
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| 653 |
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|a finite volume method
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| 653 |
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|a constraint handling
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| 653 |
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|a Research & information: general / bicssc
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| 653 |
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|a region of interest approximation
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| 653 |
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|a multiobjective descent
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| 653 |
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|a decision-making process
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| 653 |
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|a decision making process
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| 653 |
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|a adaptive algorithm
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| 653 |
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|a base excitation
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| 653 |
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|a Template-Based Modeling
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| 653 |
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|a Monte Carlo analysis
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| 653 |
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|a ensemble method
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| 653 |
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|a liquid storage tanks
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| 653 |
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|a overflooding
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| 653 |
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|a incorporation of preferences
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| 653 |
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|a optimization using preferences
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| 653 |
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|a artificial intelligence
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| 653 |
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|a Multi-Gene Genetic Programming
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| 653 |
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|a VCO
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| 653 |
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|a chaotic perturbation
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| 653 |
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|a fixed point arithmetic
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| 653 |
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|a optimization framework
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| 653 |
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|a optimization
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| 653 |
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|a energy central
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| 653 |
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|a multi-criteria classification
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| 653 |
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|a derivative-free optimization
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| 653 |
|
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|a recommender system
|
| 700 |
1 |
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|a Schütze, Oliver
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| 700 |
1 |
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|a Ruiz, Juan Gabriel
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| 700 |
1 |
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|a de la Fraga, Luis Gerardo
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
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| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-1670-7
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76746
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/4195
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 000
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|a 576
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|a 333
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|a 500
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|a 700
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|a This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
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