Professor Pietro Oliveto

PhD

Department of Computer Science

Professor in Computer Science

Programme Lead BSc/MComp Computer Science

Head of the Algorithms research group

Member of the Algorithms and Testing research groups

p.oliveto@sheffield.ac.uk

Regent Court (DCS)

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

Lissovoi A, Oliveto PS & Warwicker JA (2022) When move acceptance selection hyper-heuristics outperform metropolis and elitist evolutionary algorithms and when not. Artificial Intelligence.
Hall GT, Oliveto PS & Sudholt D (2022) On the impact of the performance metric on efficient algorithm configuration. Artificial Intelligence, 303.
Oliveto PS, Sudholt D & Witt C (2021) Tight bounds on the expected runtime of a standard steady state genetic algorithm. Algorithmica.
Corus D, Oliveto PS & Yazdani D (2021) Fast immune system-inspired hypermutation operators for combinatorial optimization. IEEE Transactions on Evolutionary Computation, 25(5), 956-970.
Oliveto P, Lissovoi A, Corus D & Witt C (2021) On steady-state evolutionary algorithms and selective pressure: Why inverse rank-based allocation of reproductive trials is best. ACM Transactions on Evolutionary Learning and Optimization, 1(1). View this article in WRRO
Oliveto PS, Auger A, Chicano F & Fonseca CM (2020) Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation. IEEE Transactions on Evolutionary Computation, 24(6), 993-994.
Corus D, Oliveto PS & Yazdani D (2020) When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms. Theoretical Computer Science, 832, 166-185. View this article in WRRO
Oliveto PS & Sutton AM (2020) Theory of evolutionary computation – Special Issue Editorial. Theoretical Computer Science, 832, 1-2.
Lissovoi A, Oliveto P & Warwicker JA (2020) Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes. Evolutionary Computation, 28(3), 437-461. View this article in WRRO
Corus D & Oliveto PS (2020) On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms. Algorithmica. View this article in WRRO
Hall GT, Oliveto PS & Sudholt D (2020) Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. Proceedings of the 2020 Genetic and Evolutionary Computation Conference.
Lissovoi A & Oliveto PS (2020) Computational Complexity Analysis of Genetic Programming, 475-518. View this article in WRRO
Lissovoi A & Oliveto PS (2019) On the time and space complexity of genetic programming for evolving Boolean conjunctions. Journal of Artificial Intelligence Research, 66, 655-689. View this article in WRRO
Corus D, Oliveto PS & Yazdani D (2019) Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem. Artificial Intelligence, 274, 180-196. View this article in WRRO
Oliveto PS, Sudholt D & Zarges C (2019) On the benefits and risks of using fitness sharing for multimodal optimisation. Theoretical Computer Science, 773, 53-70. View this article in WRRO
Nallaperuma S, Oliveto PS, Perez Heredia J & Sudholt D (2019) On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?. Algorithmica, 81(2), 858-885. View this article in WRRO
Oliveto PS, Auger A, Chicano F & Fonseca CM (2018) IEEE Transactions on Evolutionary Computation: Special Issue on Theoretical Foundations of Evolutionary Computation. IEEE Transactions on Evolutionary Computation, 22(4), 632.
Corus D, Oliveto PS & Yazdani D (2018) Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Partition Problem.. PPSN (2), 11102, 16-28.
Corus D, Oliveto PS & Yazdani D (2018) Fast Artificial Immune Systems, 67-78.
Corus D & Oliveto PS (2017) Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 22(5), 720-732. View this article in WRRO
Oliveto PS, Paixão T, Pérez Heredia J, Sudholt D & Trubenová B (2017) How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism. Algorithmica. View this article in WRRO
Dang D-C, Friedrich T, Kötzing T, Krejca M, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2017) Escaping Local Optima Using Crossover with Emergent Diversity. IEEE Transactions on Evolutionary Computation, 22(3), 484-497. View this article in WRRO
Corus D, He J, Jansen T, Oliveto PS, Sudholt D & Zarges C (2017) On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica, 78(2), 714-740. View this article in WRRO
Oliveto PS & Witt C (2015) Improved time complexity analysis of the Simple Genetic Algorithm. Theoretical Computer Science, 605, 21-41.
Oliveto PS & Sutton AM (2015) Editorial for the Special Issue on Theory of Evolutionary Algorithms 2014. Evolutionary Computation, 23(4), 509-511.
Oliveto PS & Zarges C (2015) Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change. Theoretical Computer Science, 561, 37-56.
Oliveto PS & Witt C (2014) On the runtime analysis of the Simple Genetic Algorithm. Theoretical Computer Science, 545, 2-19.
(2012) Preface. Journal of Computer Science and Technology, 27(5), 903-906.
Oliveto P & Witt, C (2011) Simpliﬁed Drift Analysis for Proving Lower Bounds in Evolutionary Computation. Algorithmica, 59, 369-386.
Oliveto P (2009) Analysis of the (1+1)-EA for Finding Approximate Solutions to Vertex Cover Problems. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 13, NO. 5, OCTOBER 2009, 13(5), 1006-1029.
Oliveto PS, He J & Yao X (2009) Analysis of the $(1+1)$-EA for Finding Approximate Solutions to Vertex Cover Problems. IEEE Transactions on Evolutionary Computation, 13(5), 1006-1029.
Friedrich T, Oliveto PS, Sudholt D & Witt C (2009) Analysis of Diversity-Preserving Mechanisms for Global Exploration. Evolutionary Computation, 17(4), 455-476.
Oliveto PS, He J & Yao X (2007) Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results. International Journal of Automation and Computing, 4(3), 281-293.

Chapters

Lehre PK & Oliveto PS (2018) Theoretical analysis of stochastic search algorithms In Martí R, Panos P & Resende MGC (Ed.), Handbook of Heuristics Springer International Publishing View this article in WRRO
Oliveto PS & Yao X (2011) Runtime Analysis of Evolutionary Algorithms for Discrete Optimization, Series on Theoretical Computer Science (pp. 21-52). WORLD SCIENTIFIC
Oliveto PS & Yao X (2011) Runtime Analysis of Evolutionary Algorithms for Discrete Optimization, Theory of Randomized Search Heuristics: Foundations and Recent Developments (pp. 21-52).

Conference proceedings papers

Lehre PK & Oliveto PS (2022) Runtime analysis of population-based evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Corus D, Oliveto PS & Yazdani D (2021) Automatic adaptation of hypermutation rates for multimodal optimisation (pp 1-12)
Lehre PK & Oliveto PS (2021) Runtime analysis of population-based evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Lehre PK & Oliveto PS (2021) Runtime analysis of evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Lehrexwid PK & Oliveto PS (2020) Runtime analysis of population-based evolutionary algorithms. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Lissovoi A, Oliveto P & Warwicker JA (2020) How the duration of the learning period affects the performance of random gradient selection hyper-heuristics. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34(03) (pp 2376-2383). New York, NY, USA, 7 February 2020 - 12 February 2020. View this article in WRRO
Foster M, Hughes M, O'Brien G, Oliveto PS, Pyle J, Sudholt D & Williams J (2020) Do sophisticated evolutionary algorithms perform better than simple ones?. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Oliveto PS, Sudholt D & Witt C (2020) A tight lower bound on the expected runtime of standard steady state genetic algorithms. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Lehre PK & Oliveto PS (2019) Runtime analysis of evolutionary algorithms: basic introduction. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Corus D & Oliveto PS (2019) On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms. Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '19, 13 July 2019 - 17 July 2019. View this article in WRRO
Hall GT, Oliveto P & Sudholt D (2019) On the impact of the cutoff time on the performance of algorithm configurators. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2019). Prague, Czech Republic, 13 July 2019 - 17 July 2019. View this article in WRRO
Corus D, Oliveto PS & Yazdani D (2019) On inversely proportional hypermutations with mutation potential. Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '19, 13 July 2019 - 17 July 2019. View this article in WRRO
Doerr B, Lissovoi A & Oliveto PS (2019) Evolving boolean functions with conjunctions and disjunctions via genetic programming. GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference. Prague, Czech Republic, 13 July 2019 - 17 July 2019. View this article in WRRO
Corus D, Oliveto PS & Yazdani D (2019) On inversely proportional hypermutations with mutation potential.. GECCO (pp 215-223)
Doerr B, Lissovoi A & Oliveto PS (2019) Evolving boolean functions with conjunctions and disjunctions via genetic programming.. GECCO (pp 1003-1011)
Corus D, Oliveto PS & Yazdani D (2018) Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Partition Problem. Parallel Problem Solving from Nature – PPSN XV, PT II, Vol. 11102 (pp 16-28). Coimbra, Portugal, 8 September 2018 - 12 September 2018. View this article in WRRO
Lehre PK & Oliveto PS (2018) Runtime analysis of evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Doerr B, Oliveto PS, Lissovoi A & Warwicker JA (2018) On the runtime analysis of selection hyper-heuristics with adaptive learning periods. GECCO 2018 Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp 1015-1022) View this article in WRRO
Corus D, Oliveto PS & Yazdani D (2018) Fast Artificial Immune Systems.. PPSN (2), Vol. 11102 (pp 67-78)
Pappa GL, Emmerich MTM, Bazzan A, Browne W, Deb K, Doerr C, Ðurasević M, Epitropakis MG, Haraldsson SO, Jakobovic D , Kerschke P et al (2018) Tutorials at PPSN 2018 (pp 477-489)
Purshouse R, Zarges C, Cussat-Blanc S, Epitropakis MG, Gallagher M, Jansen T, Kerschke P, Li X, Lobo FG, Miller J , Oliveto PS et al (2018) Workshops at PPSN 2018 (pp 490-497)
Corus D & Oliveto PS (2018) Standard steady state genetic algorithms can hillclimb faster than evolutionary algorithms using standard bit mutation. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Lissovoi A & Oliveto PS (2018) On the time and space complexity of genetic programming for evolving Boolean conjunctions. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp 1363-1370) View this article in WRRO
Lehre PK & Oliveto PS (2017) Runtime analysis of population-based evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion
Corus D, Oliveto PS & Yazdani D (2017) On the runtime analysis of the opt-IA artificial immune system. Proceedings of the Genetic and Evolutionary Computation Conference
Nallaperuma S, Oliveto PS, Perez Heredia J & Sudholt D (2017) When is it Beneficial to Reject Improvements?. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 17) (pp 1391-1398), 15 July 2017 - 19 July 2017. View this article in WRRO
Lissovoi A, Oliveto PS & Warwicker JA (2017) On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation. GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp 849-856), 15 July 2017 - 19 July 2017. View this article in WRRO
Dang D, Friedrich T, Kötzing T, Krejca MS, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2016) Emergence of Diversity and its Benefits for Crossover in Genetic Algorithms. Parallel Problem Solving from Nature – PPSN XIV View this article in WRRO
Lehre PK & Oliveto PS (2016) Runtime Analysis of Population-based Evolutionary Algorithms. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion, 20 July 2016 - 24 July 2016.
Oliveto PS, Paixao T, Perez Heredia J, Sudholt D & Trubenova B (2016) When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys. GECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016 (pp 1163-1170), 20 July 2016 - 24 July 2016. View this article in WRRO
(2016) Genetic Programming View this article in WRRO
Dang D-C, Friedrich T, Kötzing T, Krejca MS, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2016) Escaping Local Optima with Diversity Mechanisms and Crossover. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16, 20 July 2016 - 24 July 2016.
Doerr C, Bredeche N, Alba E, Bartz-Beielstein T, Brockhoff D, Doerr B, Eiben G, Epitropakis MG, Fonseca CM, Guerreiro A , Haasdijk E et al (2016) Tutorials at PPSN 2016 (pp 1012-1022)
Mambrini A & Oliveto PS (2016) On the Analysis of Simple Genetic Programming for Evolving Boolean Functions (pp 99-114)
Lehre PK & Oliveto PS (2015) Runtime Analysis of Evolutionary Algorithms. Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 11 July 2015 - 15 July 2015.
Corus D, He J, Jansen T, Oliveto PS, Sudholt D & Zarges C (2015) On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations. Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 11 July 2015 - 15 July 2015.
Oliveto PS & Sudholt D (2014) On the runtime analysis of stochastic ageing mechanisms. Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14, 12 July 2014 - 16 July 2014.
Oliveto PS, Sudholt D & Zarges C (2014) On the Runtime Analysis of Fitness Sharing Mechanisms (pp 932-941)
Lehre PK & Oliveto PS (2014) Runtime analysis of evolutionary algorithms. Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 12 July 2014 - 16 July 2014.
Lehre PK & Oliveto PS (2013) Runtime analysis of evolutionary algorithms. Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO '13 Companion, 6 July 2013 - 10 July 2013.
Jansen T, Oliveto PS & Zarges C (2013) Approximating vertex cover using edge-based representations. Proceedings of the twelfth workshop on Foundations of genetic algorithms XII - FOGA XII '13, 16 January 2013 - 20 January 2013.
Oliveto PS & Zarges C (2013) Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change. Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13, 6 July 2013 - 10 July 2013.
Oliveto PS & Witt C (2013) Improved runtime analysis of the simple genetic algorithm. Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13, 6 July 2013 - 10 July 2013.
Athanasiou A, De Felice M, Oliveto G & Oliveto PS (2013) Dynamical Modeling and Parameter Identification of Seismic Isolation Systems by Evolution Strategies (pp 101-118)
Oliveto PS & Witt C (2012) On the analysis of the simple genetic algorithm. Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12, 7 July 2012 - 11 July 2012.
Jansen T, Oliveto PS & Zarges C (2011) On the Analysis of the Immune-Inspired B-Cell Algorithm for the Vertex Cover Problem (pp 117-131)
Athanasiou A, De Felice M, Oliveto G & Oliveto PS (2011) Evolutionary algorithms for the identification of structural systems in earthquake engineering. ECTA 2011 FCTA 2011 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications (pp 52-62)
Neumann F, Oliveto PS, Rudolph G & Sudholt D (2011) On the effectiveness of crossover for migration in parallel evolutionary algorithms. Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11, 12 July 2011 - 16 July 2011.
Kratsch S, Lehre PK, Neumann F & Oliveto PS (2010) Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation (pp 204-213)
Chandra A, Oliveto PS & Yao X (2010) Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game (pp 61-70)
Kötzing T, Lehre PK, Neumann F & Oliveto PS (2010) Ant colony optimization and the minimum cut problem. Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10, 7 July 2010 - 11 July 2010.
Oliveto PS, Lehre PK & Neumann F (2009) Theoretical analysis of rank-based mutation - combining exploration and exploitation. 2009 IEEE Congress on Evolutionary Computation, 18 May 2009 - 21 May 2009.
Neumann F, Oliveto PS & Witt C (2009) Theoretical analysis of fitness-proportional selection. Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09, 8 July 2009 - 12 July 2009.
Oliveto PS & Witt C (2008) Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation (pp 82-91)
Oliveto PS, Jun He & Xin Yao (2008) Analysis of population-based evolutionary algorithms for the vertex cover problem. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1 June 2008 - 6 June 2008.
Friedrich T, Oliveto PS, Sudholt D & Witt C (2008) Theoretical analysis of diversity mechanisms for global exploration. Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08, 12 July 2008 - 16 July 2008.
Cutello V, Nicosia G, Romeo M & Oliveto PS (2007) On the Convergence of Immune Algorithms. 2007 IEEE Symposium on Foundations of Computational Intelligence, 1 April 2007 - 5 April 2007.
Oliveto PS, He J & Yao X (2007) Evolutionary algorithms and the Vertex Cover problem. 2007 IEEE Congress on Evolutionary Computation, 25 September 2007 - 28 September 2007.
Cutello V, Nicosia G & Oliveto PS (2006) Analysis of an evolutionary algorithm with HyperMacromutation and stop at first constructive mutation heuristic for solving trap functions. Proceedings of the 2006 ACM symposium on Applied computing - SAC '06, 23 April 2006 - 27 April 2006.
Lissovoi A, Oliveto PS & Warwicker JA () On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33 (pp 2322-2329)
Hall GT, Oliveto PS & Sudholt D () Fast Perturbative Algorithm Configurators. Parallel Problem Solving from Nature View this article in WRRO
Hall G, Oliveto PS & Sudholt D () Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators. Proceedings of the Genetic and Evolutionary Computation Conference View this article in WRRO

Working papers

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

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.
Corus D, Oliveto PS & Yazdani D (2020) Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation, arXiv.
Hall GT, Oliveto PS & Sudholt D (2020) Fast Perturbative Algorithm Configurators, arXiv.
Hall GT, Oliveto PS & Sudholt D (2020) Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators, arXiv.
Hall GT, Oliveto PS & Sudholt D (2019) On the Impact of the Cutoff Time on the Performance of Algorithm Configurators, arXiv.
Doerr B, Lissovoi A & Oliveto PS (2019) Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming, arXiv.
Corus D, Oliveto PS & Yazdani D (2019) On Inversely Proportional Hypermutations with Mutation Potential, arXiv.
Corus D & Oliveto PS (2019) On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms, arXiv.
Lissovoi A & Oliveto PS (2018) Computational Complexity Analysis of Genetic Programming, arXiv.
Corus D, Oliveto PS & Yazdani D (2018) Fast Artificial Immune Systems, arXiv.
Corus D, Oliveto PS & Yazdani D (2018) Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem, arXiv.
Corus D, Oliveto PS & Yazdani D (2018) When Hypermutations and Ageing Enable Artificial Immune Systems to Outperform Evolutionary Algorithms, arXiv.
Lissovoi A, Oliveto PS & Warwicker JA (2018) Simple Hyper-heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes, arXiv.
Lehre PK & Oliveto PS (2017) Theoretical Analysis of Stochastic Search Algorithms, arXiv.
Corus D & Oliveto PS (2017) Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms, arXiv.
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.

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