A survey on evolutionary machine learning

Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions wit...

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Veröffentlicht in:Journal of the Royal Society of New Zealand Jg. 49; H. 2; S. 205 - 228
Hauptverfasser: Al-Sahaf, Harith, Bi, Ying, Chen, Qi, Lensen, Andrew, Mei, Yi, Sun, Yanan, Tran, Binh, Xue, Bing, Zhang, Mengjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Wellington Taylor & Francis 01.06.2019
Taylor & Francis Ltd
Schlagworte:
Artificial intelligence
classification
Clustering
Combinatorial analysis
combinatorial optimisation
Computation
Computer vision
Dairy industry
Deep learning
ensemble learning
Evolution
Evolutionary algorithms
Evolutionary computation
Industry
Learning
Learning algorithms
Machine learning
Optimization
regression
Seafood
Seafoods
Supply chains
Surveying
Transfer learning
Wine
New Zealand
ISSN:0303-6758, 1175-8899, 1175-8899
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e. evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning.
Bibliographie:Archived by the National Library of New Zealand
Includes illustrations, references; special issue, Nga kete: the 2019 annual collection of reviews
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0303-6758
1175-8899
1175-8899
DOI:10.1080/03036758.2019.1609052