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A Survey on Evolutionary Machine Learning

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journal contribution
posted on 16.06.2020 by Harith Al-Sahaf, Ying Bi, Qi Chen, Andrew Lensen, Yi Mei, Yanan Sun, Binh Tran, Bing Xue, Mengjie Zhang
© 2019, © 2019 The Royal Society of New Zealand. 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.

History

Preferred citation

Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., Tran, B., Xue, B. & Zhang, M. (2019). A Survey on Evolutionary Machine Learning. Journal of the Royal Society of New Zealand, 49(2), 205-228. https://doi.org/10.1080/03036758.2019.1609052

Journal title

Journal of the Royal Society of New Zealand

Volume

49

Issue

2

Publication date

01/04/2019

Pagination

205-228

Publisher

Taylor & Francis Production

Publication status

Published

Online publication date

05/05/2019

ISSN

0303-6758

eISSN

1175-8899

Language

en

Exports