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210512 ||| eng |
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|a books978-3-03943-612-5
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|a 9783039436125
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|a 9783039436118
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| 100 |
1 |
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|a Milani, Alfredo
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| 245 |
0 |
0 |
|a Evolutionary Algorithms in Intelligent Systems
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (144 p.)
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a particle swarm optimization (PSO)
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| 653 |
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|a parameter analysis
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| 653 |
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|a sequence traversal
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| 653 |
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|a vertical union
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| 653 |
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|a n/a
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| 653 |
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|a big data
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| 653 |
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|a Gaussian mutation
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| 653 |
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|a global continuous optimization
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| 653 |
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|a PSO
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| 653 |
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|a neural networks
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| 653 |
|
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|a parameter puning
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| 653 |
|
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|a neuroevolution
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| 653 |
|
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|a formal methods in evolutionary algorithms
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| 653 |
|
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|a ensemble of constraint handling techniques
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| 653 |
|
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|a multi-objective optimization
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| 653 |
|
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|a Information technology industries / bicssc
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| 653 |
|
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|a evolutionary algorithms
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| 653 |
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|a differential evolution
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| 653 |
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|a wireless sensor networks
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| 653 |
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|a hybrid algorithms
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| 653 |
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|a constrained optimization
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| 653 |
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|a social network optimization
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| 653 |
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|a task allocation
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| 653 |
|
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|a self-adaptive differential evolutionary algorithms
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| 653 |
|
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|a adaptive local search operator
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| 653 |
|
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|a stochastic optimization
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| 653 |
|
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|a multi-objective optimization problems
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| 653 |
|
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|a association rules
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| 653 |
|
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|a horizontal union
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| 653 |
|
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|a improved learning strategy
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| 653 |
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|a co-evolution
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| 653 |
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|a memetic particle swarm optimization
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| 653 |
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|a mining algorithm
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| 653 |
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|a interval concept lattice
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| 700 |
1 |
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|a Carpi, Arturo
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| 700 |
1 |
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|a Poggioni, Valentina
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| 700 |
1 |
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|a Milani, Alfredo
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-03943-612-5
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/3184
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/69391
|z DOAB: description of the publication
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|a 000
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|a 576
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|a 700
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|a 600
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|a Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.
|