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.

Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimization, image analysis, and classification. Transfer learning is a type of machine learning approach that can be used to solve complex tasks. Transfer learning has been introduced to GP to solve complex Boolean and symbolic regression problems with some promise. However, the use of transfer learning with GP has not been investigated to address complex image classification tasks with noise and rotations, where GP cannot achieve satisfactory performance, but GP with transfer learning may improve the performance. In this paper, we propose a novel approach based on transfer learning and GP to solve complex image classification problems by extracting and reusing blocks of knowledge/information, which are automatically discovered from similar as well as different image classification tasks during the evolutionary process. The proposed approach is evaluated on three texture data sets and three office data sets of image classification benchmarks, and achieves better classification performance than the state-of-the-art image classification algorithm. Further analysis on the evolved solutions/trees shows that the proposed approach with transfer learning can successfully discover and reuse knowledge/information extracted from similar or different problems to improve its performance on complex image classification problems.

The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.

Melanoma is the deadliest form of skin cancer that causes around 75% of deaths worldwide. However, most of the skin cancers can be cured, especially if detected and treated early. Existing approaches have employed various feature extraction methods, where different types of features are used individually for skin image classification which may not provide sufficient information to the classification algorithm necessary to discriminate between classes, leading to sub-optimal performance. This study develops a novel skin image classification method using multi-tree genetic programming (GP). To capture local information from gray and color skin images, Local Binary Pattern is used in this work. In addition, for capturing global information, variation in color within the lesion and the skin regions, and domain-specific lesion border shape features are extracted. GP with a multi-tree representation is employed to use multiple types of features. Genetic operators such as crossover and mutation are designed accordingly in order to select a single type of features at terminals in one tree of the GP individual. The performance of the proposed method is assessed using two skin image datasets having images captured from multiple modalities, and compared with six most commonly used classification algorithms as well as the standard (single-tree) wrapper and embedded GP methods. The results show that the proposed method has significantly outperformed all these classification methods. Being interpretable and fast in terms of the computation time, this method can help dermatologist identify prominent skin image features, specific to a type of skin cancer in real-time situations.

Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats that are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system needs to be developed that has the ability of monitoring and capturing various types of threats, analyzing, and devising a plan to avoid further attacks. This article provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, analysis of the techniques, and evaluation methods. Last, we highlight misconceptions, insights, and limitations of this study and suggest some future work directions to address the limitations.