Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Machine Learning Methods to Analyze Migration Parameters in Parallel Genetic Algorithms
Year:2007
Research Areas
  • Architecture of computers,
  • Artificial intelligence
Information
Abstract
Parallel genetic algorithms (PGAs) are a powerful tool to deal with complex optimization problems. Nevertheless, the task of selecting its parameters accurately is an optimization problem by itself. Any additional help or hints to adjust the configuration parameters will lead both towards a more efficient PGA application and to a better comprehension on how these parameters affect optimization behavior and performance. This contribution offers an analysis on certain PGA parameters such as migration frequency, topology, connectivity and number of islands. The study has been carried out on an intensive set of experiments that collect PGA performance on several representative problems. The results have been analyzed using machine learning methods to identify behavioral patterns that are labeled as ¿good¿ PGA configurations. This study is a first step to generalize relevant patterns from the problems analyzed that identify better configurations in PGAs.
International
Si
Congress
Hybrid Artificial Intelligence Systems 2007
960
Place
Salamanca, España
Reviewers
Si
ISBN/ISSN
Start Date
12/11/2007
End Date
13/11/2007
From page
To page
Participants
  • Participante: SANTIAGO MUELAS (UPM, CESVIMA)
  • Autor: Victor Robles Forcada (UPM)
  • Autor: Antonio Latorre De la Fuente (UPM)
  • Participante: PEDRO DE MIGUEL ANASAGASTI (UPM, CESVIMA)
  • Autor: Jose Maria Peña Sanchez (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Data Mining Engineering (DaME) Ingeniería de Minería de datos
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
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