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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260921T102959Z
UID:Seminar-dept-414@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Lutz Oettershagen:MAILTO:Lutz.Oettershagen@liverpool.ac.uk
DTSTART:20160621T130000
DTEND:20160621T140000
SUMMARY:School Seminar Series
DESCRIPTION:Prof. Patrick De Causmaecker: Data science in optimisation: an example\n\nData analysis is increasingly used to support the development of algorithms. Many approaches are black box: in parameter tuning, the algorithm is exposed to a (large) number of instances in order to find the best parameter setting, in algorithm selection instance features are discovered to learn predictors of algorithm behaviour. White box approaches allow to study algorithm internals and potentially can be used as support tools for creative algorithm developers. We present an example of the latter:  \n\n\n\nCharacterization of neighborhood behaviours in a multi-neighborhood local search algorithm\n\n\n\nA multi-neighborhood local search algorithm with  a large number of possible neighborhoods is investigated. Each neighborhood  is chosen at each iteration with a set probability. These probabilities are fixed for  the algorithm run. \n\nWe propose a systematic method to characterize each neighborhood's behaviors, representing them as a feature vector, and using cluster analysis \n\nto form similar groups of neighborhoods. The novelty of our characterization method is that it reflects changes of behaviours according to hardness of different solution quality regions. We show that using neighborhood clusters instead of individual neighborhoods helps to reduce the parameter \n\nconfiguration space without misleading the search of the tuning procedure. \n\nThe method is problem-independent and can be applied in similar contexts.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=414
LOCATION:Ashton Lecture Theater
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