Empirically Adjusted Greedy Algoriths (EAGH): A new approach to solving combinatorial optimisation problems
A greedy heuristics to solve a given combinatorial optimisation problem can be seen as an element of an infinite set of heuristics, H, which is defined by a function that depends on several parameters. We propose a procedure for determining the best element of H for a set of instances of the combina...
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2005 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/510 |
| Acceso en línea: | https://hdl.handle.net/2117/510 |
| Access Level: | acceso abierto |
| Palabra clave: | Combinatorial optimisation Greedy algorithms Non-linear optimisation Optimización no lineal Optimització no lineal Optimització combinatòria Optimización combinatoria EAGH Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica discreta::Combinatòria |
| Sumario: | A greedy heuristics to solve a given combinatorial optimisation problem can be seen as an element of an infinite set of heuristics, H, which is defined by a function that depends on several parameters. We propose a procedure for determining the best element of H for a set of instances of the combinatorial optimisation problem. The procedure consists essentially in applying a direct non-linear optimization algorithm to a function of the parameters that characterise H. |
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