Numerical and Evolutionary Optimization
(Visualizza in formato marc)
(Visualizza in BIBFRAME)
| Autore: |
Lara Adriana
|
| Titolo: |
Numerical and Evolutionary Optimization
|
| Pubblicazione: | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica: | 1 electronic resource (230 p.) |
| Soggetto non controllato: | model predictive control |
| bulbous bow | |
| improvement differential evolution algorithm | |
| evolutionary multi-objective optimization | |
| location routing problem | |
| flexible job shop scheduling problem | |
| basic differential evolution algorithm | |
| metric measure spaces | |
| NEAT | |
| genetic algorithm | |
| multiobjective optimization | |
| improved differential evolution algorithm | |
| performance indicator | |
| rubber | |
| averaged Hausdorff distance | |
| mixture experiments | |
| U-shaped assembly line balancing | |
| Genetic Programming | |
| Local Search | |
| driving events | |
| surrogate-based optimization | |
| single component constraints | |
| crop planning | |
| Pareto front | |
| numerical simulations | |
| shape morphing | |
| genetic programming | |
| economic crops | |
| local search and jump search | |
| model order reduction | |
| optimal solutions | |
| EvoSpace | |
| risky driving | |
| intelligent transportation systems | |
| optimal control | |
| IV-optimality criterion | |
| Bloat | |
| decision space diversity | |
| modify differential evolution algorithm | |
| power means | |
| driving scoring functions | |
| open-source framework | |
| evolutionary computation | |
| differential evolution algorithm | |
| vehicle routing problem | |
| multi-objective optimization | |
| Persona (resp. second.): | QuirozMarcela |
| SchützeOliver | |
| Mezura-MontesEfrén | |
| Sommario/riassunto: | This book was established after the 6th 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. |
| Titolo autorizzato: | Numerical and Evolutionary Optimization |
| ISBN: | 3-03921-817-4 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910367744103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |