Optimizing initial guesses to improve global minimization
In this paper, we envision global optimization as finding, for a given calculation complexity, a suitable initial guess of a considered optimization algorithm. One can imagine that this possibility clearly improve the capacity of existing optimization algorithms, including stochastic ones. This appr...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/56490 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/56490 |
| Access Level: | acceso abierto |
| Palabra clave: | 519.863 004.421:575.8 Global optimization Dynamical Systems Semi-Deterministic Algorithms Genetic Algorithms. Investigación operativa (Matemáticas) 1207 Investigación Operativa |
| Sumario: | In this paper, we envision global optimization as finding, for a given calculation complexity, a suitable initial guess of a considered optimization algorithm. One can imagine that this possibility clearly improve the capacity of existing optimization algorithms, including stochastic ones. This approach is validated on several large dimension nonlinear minimization problems. Results are compared with those obtained by a geneti algorithm |
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