A genetic hill climbing method for function optimization using a neighborhood based on interactions among parameters

Most conventional genetic algorithms (GAs) for function optimization always search all parameters simultaneously. As the result, the search space size increases exponentially with the number of parameters. Therefore, the search efficiency of these GAs deteriorates in high-dimensional function optimi...

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Bibliographische Detailangaben

Hauptverfasser:	Takeichi, H., Mizuguchi, N., Ono, I., Ono, N.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Design methodology Encoding Genetic algorithms Genetic mutations Optimization methods Sampling methods Topology
Online-Zugang:	Volltext bestellen
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