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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Brauer, Jürgen | |
dc.date.accessioned | 2022-05-02T08:28:02Z | - |
dc.date.available | 2022-05-02T08:28:02Z | - |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9783731501848 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/146309 | - |
dc.language.iso | eng | |
dc.publisher | KIT Scientific Publishing | |
dc.relation.uri | https://mdpi.com/books/pdfview/book/1860 | |
dc.rights.uri | CC BY-NC-ND (姓名標示-非商業性-禁止改作) | |
dc.source | DOAB | |
dc.subject.classification | Computer science | |
dc.subject.other | artificial bee colony algorithm (ABC) | |
dc.subject.other | cloud model | |
dc.subject.other | normal cloud model | |
dc.subject.other | Y conditional cloud generator | |
dc.subject.other | global optimum | |
dc.subject.other | evolution | |
dc.subject.other | computation | |
dc.subject.other | urban design | |
dc.subject.other | biology | |
dc.subject.other | shape grammar | |
dc.subject.other | architecture | |
dc.subject.other | SPEA 2 | |
dc.subject.other | energy-efficient job shop scheduling | |
dc.subject.other | dispatching rule | |
dc.subject.other | nonlinear convergence factor | |
dc.subject.other | mutation operation | |
dc.subject.other | whale optimization algorithm | |
dc.subject.other | particle swarm optimization | |
dc.subject.other | confidence term | |
dc.subject.other | random weight | |
dc.subject.other | benchmark functions | |
dc.subject.other | t-test | |
dc.subject.other | success rates | |
dc.subject.other | average iteration times | |
dc.subject.other | set-union knapsack problem | |
dc.subject.other | moth search algorithm | |
dc.subject.other | transfer function | |
dc.subject.other | discrete algorithm | |
dc.subject.other | evolutionary multi-objective optimization | |
dc.subject.other | convergence point | |
dc.subject.other | acceleration search | |
dc.subject.other | evolutionary computation | |
dc.subject.other | optimization | |
dc.subject.other | bat algorithm (BA) | |
dc.subject.other | bat algorithm with multiple strategy coupling (mixBA) | |
dc.subject.other | CEC2013 benchmarks | |
dc.subject.other | Wilcoxon test | |
dc.subject.other | Friedman test | |
dc.subject.other | facility layout design | |
dc.subject.other | single loop | |
dc.subject.other | monarch butterfly optimization | |
dc.subject.other | slicing tree structure | |
dc.subject.other | material handling path | |
dc.subject.other | integrated design | |
dc.subject.other | wireless sensor networks (WSNs) | |
dc.subject.other | DV-Hop algorithm | |
dc.subject.other | multi-objective DV-Hop localization algorithm | |
dc.subject.other | NSGA-II-DV-Hop | |
dc.subject.other | first-arrival picking | |
dc.subject.other | fuzzy c-means | |
dc.subject.other | particle swarm optimization | |
dc.subject.other | range detection | |
dc.subject.other | minimum total dominating set | |
dc.subject.other | evolutionary algorithm | |
dc.subject.other | genetic algorithm | |
dc.subject.other | local search | |
dc.subject.other | constrained optimization problems (COPs) | |
dc.subject.other | evolutionary algorithms (EAs) | |
dc.subject.other | firefly algorithm (FA) | |
dc.subject.other | stochastic ranking (SR) | |
dc.subject.other | Artificial bee colony | |
dc.subject.other | swarm intelligence | |
dc.subject.other | elite strategy | |
dc.subject.other | dimension learning | |
dc.subject.other | global optimization | |
dc.subject.other | DE algorithm | |
dc.subject.other | ?-Hilbert space | |
dc.subject.other | topology structure | |
dc.subject.other | quantum uncertainty property | |
dc.subject.other | numerical simulation | |
dc.subject.other | whale optimization algorithm | |
dc.subject.other | flexible job shop scheduling problem | |
dc.subject.other | nonlinear convergence factor | |
dc.subject.other | adaptive weight | |
dc.subject.other | variable neighborhood search | |
dc.subject.other | elephant herding optimization | |
dc.subject.other | EHO | |
dc.subject.other | swarm intelligence | |
dc.subject.other | individual updating strategy | |
dc.subject.other | large-scale | |
dc.subject.other | benchmark | |
dc.subject.other | diversity maintenance | |
dc.subject.other | particle swarm optimizer | |
dc.subject.other | entropy | |
dc.subject.other | large scale optimization | |
dc.subject.other | minimum load coloring | |
dc.subject.other | memetic algorithm | |
dc.subject.other | evolutionary | |
dc.subject.other | local search | |
dc.subject.other | particle swarm optimization | |
dc.subject.other | large-scale optimization | |
dc.subject.other | adaptive multi-swarm | |
dc.subject.other | diversity maintenance | |
dc.subject.other | deep learning | |
dc.subject.other | convolutional neural network | |
dc.subject.other | rock types | |
dc.subject.other | automatic identification | |
dc.subject.other | monarch butterfly optimization | |
dc.subject.other | greedy optimization algorithm | |
dc.subject.other | global position updating operator | |
dc.subject.other | 0-1 knapsack problems | |
dc.title | Human Pose Estimation with Implicit Shape Models | |
dc.type | 電子教科書 | |
dc.classification | 自然科學類 | |
Theme: | 教科書-自然科學類 |
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