|
Descripción
|
|
|---|---|
| Searching for new efficient and exact heuristic optimization algorithms in big search spaces currently remains as an open problem. The search space increases exponentially with the problem size, making impossible to find a solution through a mere blind search. Several heuristic approaches inspired by nature have been adopted as suitable algorithms to solve complex optimization problems in many different areas. Networks of Bio-inspired Processors (NBP) is a formal framework formed of highly parallel and distributed computing models inspired and abstracted by biological evolution. From a theoretical point of view, NBP has been proved broadly to be an efficient solving of NP complete problems. The aim of this paper is to explore the expressive power of NBP to solve hard optimization problems with a big search space, using massively parallel architectures. We use the basic concepts and principles of some metaheuristic approaches to propose an extension of the NBP model, which is able to solve actual problems in the optimization field from a practical point of view. | |
|
Internacional
|
No |
|
Nombre congreso
|
13 th International Work-Conference on Artificial Neural Networks - IWANN (Alpha Core Ranking: CORE B) |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Palma de Mallorca, Sapin |
|
Revisores
|
Si |
|
ISBN o ISSN
|
978-3-319-19258-1 |
|
DOI
|
10.1007/978-3-319-19258-1_3 |
|
Fecha inicio congreso
|
10/06/2015 |
|
Fecha fin congreso
|
12/06/2015 |
|
Desde la página
|
29 |
|
Hasta la página
|
39 |
|
Título de las actas
|
Advances in Computational Intelligence Volume 9094 of the series Lecture Notes in Computer Science |