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210512 ||| eng |
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|a books978-3-03921-929-2
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|a 9783039219285
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|a 9783039219292
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100 |
1 |
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|a Alavi, Amir
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245 |
0 |
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|a Evolutionary Computation
|h Elektronische Ressource
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260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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300 |
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|a 1 electronic resource (424 p.)
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653 |
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|a benchmark
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653 |
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|a minimum total dominating set
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653 |
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|a 0-1 knapsack problems
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653 |
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|a global optimum
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653 |
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|a bat algorithm (BA)
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653 |
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|a genetic algorithm
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653 |
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|a local search
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653 |
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|a slicing tree structure
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653 |
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|a evolutionary computation
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653 |
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|a random weight
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653 |
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|a deep learning
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653 |
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|a automatic identification
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653 |
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|a material handling path
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653 |
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|a flexible job shop scheduling problem
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653 |
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|a elephant herding optimization
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653 |
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|a swarm intelligence
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653 |
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|a artificial bee colony algorithm (ABC)
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653 |
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|a evolutionary algorithm
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653 |
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|a range detection
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653 |
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|a elite strategy
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653 |
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|a average iteration times
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653 |
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|a evolutionary
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653 |
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|a global position updating operator
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653 |
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|a greedy optimization algorithm
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653 |
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|a NSGA-II-DV-Hop
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653 |
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|a adaptive multi-swarm
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653 |
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|a fuzzy c-means
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653 |
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|a DE algorithm
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653 |
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|a topology structure
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653 |
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|a ?-Hilbert space
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653 |
|
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|a evolutionary multi-objective optimization
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653 |
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|a transfer function
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653 |
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|a whale optimization algorithm
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653 |
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|a success rates
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653 |
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|a bat algorithm with multiple strategy coupling (mixBA)
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653 |
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|a DV-Hop algorithm
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653 |
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|a evolution
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653 |
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|a individual updating strategy
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653 |
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|a Friedman test
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653 |
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|a particle swarm optimizer
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653 |
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|a CEC2013 benchmarks
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653 |
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|a wireless sensor networks (WSNs)
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653 |
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|a integrated design
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653 |
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|a rock types
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653 |
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|a urban design
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653 |
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|a particle swarm optimization
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653 |
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|a nonlinear convergence factor
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653 |
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|a convergence point
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653 |
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|a dispatching rule
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653 |
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|a minimum load coloring
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653 |
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|a confidence term
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653 |
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|a large-scale optimization
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653 |
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|a benchmark functions
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653 |
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|a large-scale
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653 |
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|a memetic algorithm
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653 |
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|a Artificial bee colony
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653 |
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|a Wilcoxon test
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653 |
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|a History of engineering and technology / bicssc
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653 |
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|a stochastic ranking (SR)
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653 |
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|a multi-objective DV-Hop localization algorithm
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653 |
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|a monarch butterfly optimization
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653 |
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|a acceleration search
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653 |
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|a discrete algorithm
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653 |
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|a single loop
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653 |
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|a adaptive weight
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653 |
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|a Y conditional cloud generator
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653 |
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|a set-union knapsack problem
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653 |
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|a firefly algorithm (FA)
|
653 |
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|a variable neighborhood search
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653 |
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|a shape grammar
|
653 |
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|a constrained optimization problems (COPs)
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653 |
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|a EHO
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653 |
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|a biology
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653 |
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|a global optimization
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653 |
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|a energy-efficient job shop scheduling
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653 |
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|a large scale optimization
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653 |
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|a numerical simulation
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653 |
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|a mutation operation
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653 |
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|a architecture
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653 |
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|a facility layout design
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653 |
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|a evolutionary algorithms (EAs)
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653 |
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|a normal cloud model
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653 |
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|a dimension learning
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653 |
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|a entropy
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653 |
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|a diversity maintenance
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653 |
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|a quantum uncertainty property
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653 |
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|a SPEA 2
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653 |
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|a convolutional neural network
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653 |
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|a cloud model
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653 |
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|a first-arrival picking
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653 |
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|a optimization
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653 |
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|a computation
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653 |
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|a moth search algorithm
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653 |
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|a t-test
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700 |
1 |
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|a Wang, Gai-Ge
|
041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
989 |
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|b DOAB
|a Directory of Open Access Books
|
500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
|
028 |
5 |
0 |
|a 10.3390/books978-3-03921-929-2
|
856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/47147
|z DOAB: description of the publication
|
856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/1860
|7 0
|x Verlag
|3 Volltext
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082 |
0 |
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|a 720
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082 |
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|a 576
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082 |
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|a 900
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|a 333
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082 |
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|a 700
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|a 600
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|a 620
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|a Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,
|