Numerical and Evolutionary Optimization 2020
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 th...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (364 p.) |
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| 100 | 1 | |a Quiroz, Marcela |4 edt | |
| 245 | 1 | 0 | |a Numerical and Evolutionary Optimization 2020 |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (364 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 650 | 7 | |a Mathematics & science |2 bicssc | |
| 653 | |a robust optimization | ||
| 653 | |a differential evolution | ||
| 653 | |a ROOT | ||
| 653 | |a optimization framework | ||
| 653 | |a drainage rehabilitation | ||
| 653 | |a overflooding | ||
| 653 | |a pipe breaking | ||
| 653 | |a VCO | ||
| 653 | |a CMOS differential pair | ||
| 653 | |a PVT variations | ||
| 653 | |a Monte Carlo analysis | ||
| 653 | |a multi-objective optimization | ||
| 653 | |a Pareto Tracer | ||
| 653 | |a continuation | ||
| 653 | |a constraint handling | ||
| 653 | |a surrogate modeling | ||
| 653 | |a multiobjective optimization | ||
| 653 | |a evolutionary algorithms | ||
| 653 | |a kriging method | ||
| 653 | |a ensemble method | ||
| 653 | |a adaptive algorithm | ||
| 653 | |a liquid storage tanks | ||
| 653 | |a base excitation | ||
| 653 | |a artificial intelligence | ||
| 653 | |a Multi-Gene Genetic Programming | ||
| 653 | |a computational fluid dynamics | ||
| 653 | |a finite volume method | ||
| 653 | |a JSSP | ||
| 653 | |a CMOSA | ||
| 653 | |a CMOTA | ||
| 653 | |a chaotic perturbation | ||
| 653 | |a fixed point arithmetic | ||
| 653 | |a FP16 | ||
| 653 | |a pseudo random number generator | ||
| 653 | |a incorporation of preferences | ||
| 653 | |a multi-criteria classification | ||
| 653 | |a decision-making process | ||
| 653 | |a multi-objective evolutionary optimization | ||
| 653 | |a outranking relationships | ||
| 653 | |a decision maker profile | ||
| 653 | |a profile assessment | ||
| 653 | |a region of interest approximation | ||
| 653 | |a optimization using preferences | ||
| 653 | |a hybrid evolutionary approach | ||
| 653 | |a forecasting | ||
| 653 | |a Convolutional Neural Network | ||
| 653 | |a LSTM | ||
| 653 | |a COVID-19 | ||
| 653 | |a deep learning | ||
| 653 | |a trust region methods | ||
| 653 | |a multiobjective descent | ||
| 653 | |a derivative-free optimization | ||
| 653 | |a radial basis functions | ||
| 653 | |a fully linear models | ||
| 653 | |a decision making process | ||
| 653 | |a cognitive tasks | ||
| 653 | |a recommender system | ||
| 653 | |a project portfolio selection problem | ||
| 653 | |a usability evaluation | ||
| 653 | |a multi-objective portfolio optimization problem | ||
| 653 | |a trapezoidal fuzzy numbers | ||
| 653 | |a density estimators | ||
| 653 | |a steady state algorithms | ||
| 653 | |a protein structure prediction | ||
| 653 | |a Hybrid Simulated Annealing | ||
| 653 | |a Template-Based Modeling | ||
| 653 | |a structural biology | ||
| 653 | |a Metropolis | ||
| 653 | |a optimization | ||
| 653 | |a linear programming | ||
| 653 | |a energy central | ||
| 776 | |z 3-0365-1669-7 | ||
| 776 | |z 3-0365-1670-0 | ||
| 700 | 1 | |a Schütze, Oliver |4 edt | |
| 700 | 1 | |a Ruiz, Juan Gabriel |4 edt | |
| 700 | 1 | |a de la Fraga, Luis Gerardo |4 edt | |
| 700 | 1 | |a Quiroz, Marcela |4 oth | |
| 700 | 1 | |a Schütze, Oliver |4 oth | |
| 700 | 1 | |a Ruiz, Juan Gabriel |4 oth | |
| 700 | 1 | |a de la Fraga, Luis Gerardo |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:52:46 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338185770004498&Force_direct=true |Z 5338185770004498 |b Available |8 5338185770004498 | ||