Abstract:

The previous two decades have seen significant advances in optimisation techniques that are able to quickly find optimal or near-optimal solutions to problem instances in many combinatorial optimisation domains. Despite many successful applications of both these approaches, some common weaknesses exist in that if the nature of the problems to be solved changes over time, then algorithms needs to be at best periodically re-tuned. In the worst case, new algorithms may need to be periodically redeveloped. Furthermore, many approaches are inefficient, starting from a clean slate every time a problem is solved, therefore failing to exploit previously learned knowledge.

In contrast, in the field of machine-learning, a number of recent proposals suggest that learning algorithms should exhibit life-long learning, retaining knowledge and using it to improve learning in the future. I propose that optimisation algorithms should follow the same approach – looking to nature, we observe that the natural immune system exhibits many properties of a life-long learning system that could be exploited computationally in an optimisation framework. I will give a brief overview of the immune system, focusing on highlighting its relevant computational properties and then show how it can be used to construct a lifelong learning optimisation system. The system exploits genetic programming to continually evolve new optimisation algorithms, which form a continually adapting ensemble of optimisers. The system is shown to adapt to new problems, exhibit memory, and produce efficient and effective solutions when tested in both the bin-packing and scheduling domains.

Speaker Bio:

Emma Hart is a Professor in Natural Computation at Edinburgh Napier University in Scotland, where she also directs the Centre for Algorithms, Visualisation and Evolving Systems. Prior to that, she received a degree in Chemistry from the University of Oxford and a PhD in Artificial Immune Systems for Optimisation and Learning from the University of Edinburgh.

Her research focuses on developing novel bio-inspired techniques for solving a range of real-world optimisation and classification problems, particularly through the application of hyper-heuristic approaches and genetic programming. Her recent research explores optimisation techniques which are capable of continuously improving through experience, as well as ensemble approaches to optimisation for solving large classes of problems.

She is Editor-in-Chief of the journal Evolutionary Computation (MIT Press), ) and an elected member of the ACM SIGEVO Executive Committee. She also edits SIGEVOlution, the magazine of SIGEVO. She was General Chair of PPSN 2016, and regularly acts as Track Chair at GECCO . She has recently given keynotes at EURO 2016, Poland, and IJCCI (Maderia, 2017) on Lifelong Optimisation.

Her work is funded by both national funding agencies (EPSRC) and the European, where has recently led projects in Fundamentals of Collective Adaptive System (FOCAS) and Self-Aware systems (AWARE). She has worked with a range of real-world clients including from the Forestry Industry, Logistics and Personnel Scheduling.