Our seminar series is free and available for anyone to attend. Unless otherwise stated, seminars take place on Wednesday afternoons at 2pm in the Kilburn Building during teaching season.

If you wish to propose a seminar speaker please contact Antoniu Pop.


A Rigorous Theoretical Framework for Measuring Generalisation of Co-evolutionary Learning

  • Speaker:   Professor  Xin Yao  (University of Birmingham)
  • Host:   Ke Chen
  • 13th February 2008 at 14:15 in Lecture Theatre 1.4, Kilburn Building
Co-evolutionary learning offers a very attractive learning paradigm where how well a learner (individual) does depends on a dynamic environment that includes other learners (individuals) in the same or co-evolving populations. As the case for other types of learning, generalisation is a key issue in co-evolutionary learning [1]. Although there have been experimental studies on the robustness [2], which is closely related to our notion of generalisation, of co-evolved learners, no rigorous theoretical framework exists for measuring generalisation quantitatively in co-evolutionary learning. This talk [1] will introduce such a rigorous theoretical framework for measuring generalisation, which is applicable to any type of co-evolutionary learning, based on statistical machine learning theories. Different definitions of generalisation are discussed. Specific case studies, using iterated prisoner's dilemma games as examples, will be presented. Under the vigorous theoretical framework, we are able to estimate generalisation in co-evolutionary learning within certain bounds. Such estimation represents a major step forward in measuring generalisation for co-evolutionary learning in practice.


[1] S. Y. Chong, P. Tino and X. Yao, "Measuring Generalization Performance in Co-evolutionary Learning,"IEEE Transactions on Evolutionary Computation, accepted. Appeared online:

[2] P. Darwen and X. Yao, ``On evolving robust strategies for iterated prisoner's dilemma, ''In Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, Vol. 956, Springer-Verlag, Heidelberg, Germany, pp.276-292, 1995.
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