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Zhao et al., 2020 - Google Patents

A novel modified tree‐seed algorithm for high‐Dimensional optimization problems

Zhao et al., 2020

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Document ID
1147095701979166674
Author
Zhao S
Gao L
Tu J
Yu D
Publication year
Publication venue
Chinese Journal of Electronics

External Links

Snippet

To efficiently handle high‐dimensional continuous optimization problems, a Modified tree‐ seed algorithm (MTSA) is proposed by coupling a newly introduced control parameter named as Seed domain shrinkable coefficient (SDSC) and Local reinforcement strategy …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

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    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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    • G06N3/02—Computer systems based on biological models using neural network models
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    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
    • G—PHYSICS
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