Why advanced population initialization techniques perform poorly in high dimension?

Kazimipour, B, Li, X and Qin, K 2014, 'Why advanced population initialization techniques perform poorly in high dimension?', in Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen, Tan Ke Tang (ed.) Proceedings of the Tenth International Conference on Simulated Evolution and Learning (SEAL 2014) [LNCS 8886], Dunedin, New Zealand, 15-18 December 2014, pp. 479-490.


Document type: Conference Paper
Collection: Conference Papers

Title Why advanced population initialization techniques perform poorly in high dimension?
Author(s) Kazimipour, B
Li, X
Qin, K
Year 2014
Conference name SEAL 2014
Conference location Dunedin, New Zealand
Conference dates 15-18 December 2014
Proceedings title Proceedings of the Tenth International Conference on Simulated Evolution and Learning (SEAL 2014) [LNCS 8886]
Editor(s) Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen, Tan Ke Tang
Publisher Springer Verlag
Place of publication Switzerland
Start page 479
End page 490
Total pages 12
Abstract Many advanced population initialization techniques for Evolutionary Algorithms (EAs) have hitherto been proposed. Several studies claimed that the techniques significantly improve EAs' performance. However, recent researches show that they cannot scale well to high dimensional spaces. This study investigates the reasons behind the failure of advanced population initialization techniques in large-scale problems by adopting a wide range of population sizes. To avoid being biased to any particular EA model or problem set, this study employs general purpose tools in the experiments. Our investigations show that, in spite of population size, uniformity of populations drops dramatically when dimensionality grows. The observation confirms that the uniformity loss exist in high dimensional spaces regardless of the type of EA, initializer or problem. Therefore, we conclude that the weak uniformity of the resulting population is the main cause of the poor performance of advanced initializers in high dimensions.
Subjects Neural, Evolutionary and Fuzzy Computation
Optimisation
Keyword(s) Population Initialization
Large-Scale Optimization
Evolutionary Algorithm
Uniformity
DOI - identifier 10.1007/978-3-319-13563-2
Copyright notice © Springer International Publishing Switzerland 2014
ISBN 9783319135632
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