The Evolutionary Computing and Machine Learning (ECML) group at RMIT University studies and develops nature-inspired computational models and algorithms, especially in the areas of evolutionary computation and machine learning and applies them to real-world problems. The group takes an inter-disciplinary approach drawing its inspirations from mathematical programming, meta-heuristics, and operations research. Its current research areas include: large-scale optimization, reinforcement learning, computational intelligence techniques for games, deep learning, multiobjective optimization, data mining and analytics, computer vision, time series analysis, evolutionary art, journey planning, social network analysis, itinerary recommendation, vehicle routing, and swarm intelligence.
Staff: Vic Ciesielski, Xiaodong Li, Andy Song, Fabio Zambetta, Kai Qin, Jeffrey Chan, and William Raffe.
PhD Scholarship: we are currently looking for a PhD candidate with strong math and optimization background, to work on an ARC Disovery Grant funded project on large-scale optimization using hybrid and decomposition methods. See this page for further information.