Dr Pietro Oliveto

Senior Lecturer

Telephone: +44 (0) 114 222
Emailp.oliveto@sheffield.ac.uk

Member of the Algorithms and Testing research groups
Personal website: staffwww.dcs.shef.ac.uk/people/P.Oliveto/

ORCID | Google scholar

Selected publications | All publications

Pietro Oliveto
Profile

Biography

Pietro Oliveto is a Senior Lecturer in the Algorithms group and leader of the 'Rigourous Runtime Analysis of Bio-inspired Computing' project team. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. From October 2007 to April 2008, he was a visiting researcher of the Efficient Algorithms and Complexity Theory Institute at the Department of Computer Science of the University of Dortmund where he collaborated with Prof. Ingo Wegener's research group. From 2009 to 2010 he was a Research Fellow at Birmingham funded by EPSRC under the PhD+ funding scheme. From 2010 to 2013 he was an EPSRC funded Postdoctoral Fellow in Theoretical Computer Science at the University of Birmingham, UK. In October 2013 he moved to Sheffield where he was appointed Vice-Chancellor's Fellow in the department of Computer Science. Since March 2015 he is an EPSRC Early Career Fellow.

Other professional activities and achievements:

  • Steering Committee of the workshop on Theory of Randomized Search Heuristics (ThRaSH)
  • Member of the EPSRC Review College
  • Member of ACM
  • Senior Member of IEEE
  • Member of the IEEE Computational Intelligence Society (CIS) Technical Committee on Evolutionary Computation
  • Chair of the IEEE CIS Force on Theory of Bio-inspired Computation
Research

Research

Pietro Oliveto's research interests are in bio-inspired computation, randomized search heuristics and combinatorial optimization. His main expertise is the time complexity analysis of bio-inspired search heuristics such as evolutionary algorithms, genetic algorithms and artificial immune systems. Such analyses shed light on the behaviour and performance of the heuristics for different classes of problems. By explaining how the expected optimisation time depends on problem and algorithmic characteristics, informed choices may be made concerning which heuristic to choose for a problem at hand and how to set its parameters.

Grants

Grants