So much of what Engineers design and build today takes inspiration from nature. Boeing added flaps on the wings of their planes mimicking Eagles, the shape of Whale fins helped reduce drag on wind turbines and the noses of bullet trains look suspiciously like a Kingfisher's beak. Nature has often already found elegant solutions to problems that our best and brightest work on every day. Nature finds these solutions through a process of natural selection where the genes of the best performing organisms are passed on to successive generations. "Can we use Evolution to autonomously design and build Machine Learning algorithms?" Evolutionary Machine Learning uses Darwinian natural selection to autonomously design, train and optimise Neural Networks.

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.

Real-Time Strategy (RTS) games involve multiple agents acting simultaneously, and result in enormous state dimensionality. In this paper, we propose an abstracted and simplified model for the famous game StarCraft, and design a dynamic programming algorithm to solve the building order problem, which takes minimal time to achieve a specific target. In addition, Genetic Algorithms (GA) are used to find an optimal target for the opening stage.