Scaling up evolutionary programming algorithms

Scaling up evolutionary programming algorithms

Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigate...

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Bibliographische Detailangaben
Hauptverfasser: Yao, Xin, Liu, Yong
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Learning and adaptive systems
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Beschreibung
Zusammenfassung:Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four different EP algorithms for large-scale problems, i.e., problems whose dimension ranges from 100 to 300. The four are classical EP (CEP) [1, 2], fast EP (FEP).
ISSN:0302-9743
1611-3349
DOI:10.1007/BFb0040764