A survey on evolutionary machine learning. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Al-Sahaf, Harith; Bi, Ying; Chen, Qi; Lensen, Andrew; Mei, Yi; Sun, Yanan; Tran, Binh; Xue, Bing; Zhang, Mengjie
• Is Part Of: Journal of the Royal Society of New Zealand. Volume 49:Issue 2(2019)
• Page Start: 205
• Page End: 228
• Publication Date: 2019-04-03
• Subjects: Artificial intelligence -- machine learning -- evolutionary computation -- classification -- regression -- clustering -- combinatorial optimisation -- deep learning -- transfer learning -- ensemble learning; Science -- Periodicals; 505
• DOI: 10.1080/03036758.2019.1609052
• Languages: English
• ISSNs: 0303-6758

Abstract

ABSTRACT: 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.