Professor Pietro Oliveto

PhD

Department of Computer Science

Professor in Computer Science

Programme Lead BSc/MComp Computer Science

Member of the Foundations of Computation and Testing research groups

Pietro Oliveto profile photo
Profile picture of Pietro Oliveto profile photo
p.oliveto@sheffield.ac.uk

Full contact details

Professor Pietro Oliveto
Department of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP
Profile

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.

Research interests

Pietro Oliveto's research interests are in bio-inspired computation, randomised search heuristics and combinatorial optimisation. 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.

Publications

Journal articles

Chapters

Conference proceedings papers

Working papers

  • Oliveto PS & Witt C () Erratum: Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation. RIS download Bibtex download

Preprints

  • Corus D, Lissovoi A, Oliveto PS & Witt C (2021) On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best, arXiv. RIS download Bibtex download
  • Corus D, Oliveto PS & Yazdani D (2020) Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation, arXiv. RIS download Bibtex download
  • Hall GT, Oliveto PS & Sudholt D (2020) Fast Perturbative Algorithm Configurators, arXiv. RIS download Bibtex download
  • Hall GT, Oliveto PS & Sudholt D (2020) Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators, arXiv. RIS download Bibtex download
  • Hall GT, Oliveto PS & Sudholt D (2019) On the Impact of the Cutoff Time on the Performance of Algorithm Configurators, arXiv. RIS download Bibtex download
  • Doerr B, Lissovoi A & Oliveto PS (2019) Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming, arXiv. RIS download Bibtex download
  • Corus D, Oliveto PS & Yazdani D (2019) On Inversely Proportional Hypermutations with Mutation Potential, arXiv. RIS download Bibtex download
  • Corus D & Oliveto PS (2019) On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms, arXiv. RIS download Bibtex download
  • Lissovoi A & Oliveto PS (2018) Computational Complexity Analysis of Genetic Programming, arXiv. RIS download Bibtex download
  • Corus D, Oliveto PS & Yazdani D (2018) Fast Artificial Immune Systems, arXiv. RIS download Bibtex download
  • Corus D, Oliveto PS & Yazdani D (2018) Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem, arXiv. RIS download Bibtex download
  • Corus D, Oliveto PS & Yazdani D (2018) When Hypermutations and Ageing Enable Artificial Immune Systems to Outperform Evolutionary Algorithms, arXiv. RIS download Bibtex download
  • Lissovoi A, Oliveto PS & Warwicker JA (2018) Simple Hyper-heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes, arXiv. RIS download Bibtex download
  • Lehre PK & Oliveto PS (2017) Theoretical Analysis of Stochastic Search Algorithms, arXiv. RIS download Bibtex download
  • Corus D & Oliveto PS (2017) Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms, arXiv. RIS download Bibtex download
  • Dang D-C, Friedrich T, Kötzing T, Krejca MS, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2016) Escaping Local Optima using Crossover with Emergent or Reinforced Diversity, arXiv. RIS download Bibtex download
Grants

Rigorous Runtime Analysis of Bio-Inspired Computing, EPSRC, 03/2015 to 09/2020, £1,266,592, as PI

Professional activities and memberships
  • Member of the Algorithms and Testing research groups
  • 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