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Qin et al., 2024 - Google Patents

Expensive many-objective evolutionary optimization guided by two individual infill criteria

Qin et al., 2024

Document ID
4413765560074484327
Author
Qin S
Sun C
Akhtar F
Xie G
Publication year
Publication venue
Memetic Computing

External Links

Snippet

Recently, surrogate-assisted multi-objective evolutionary algorithms have achieved much attention for solving computationally expensive multi-/many-objective optimization problems. An effective infill sampling strategy is critical in surrogate-assisted multi-objective …
Continue reading at link.springer.com (other versions)

Classifications

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    • 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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