| Level-based analysis of genetic algorithms and other search processes D Corus, DC Dang, AV Eremeev, PK Lehre IEEE Transactions on Evolutionary Computation 22 (5), 707-719, 2017 | 56 | 2017 |
| Standard steady state genetic algorithms can hillclimb faster than mutation-only evolutionary algorithms D Corus, PS Oliveto IEEE Transactions on Evolutionary Computation 22 (5), 720-732, 2017 | 52 | 2017 |
| Level-based analysis of genetic algorithms and other search processes D Corus, DC Dang, AV Eremeev, PK Lehre International Conference on Parallel Problem Solving from Nature, 912-921, 2014 | 48 | 2014 |
| Toward a unifying framework for evolutionary processes T Paixão, G Badkobeh, N Barton, D Çörüş, DC Dang, T Friedrich, ... Journal of Theoretical Biology 383, 28-43, 2015 | 24 | 2015 |
| On the runtime analysis of the opt-IA artificial immune system D Corus, PS Oliveto, D Yazdani Proceedings of the Genetic and Evolutionary Computation Conference, 83-90, 2017 | 19 | 2017 |
| Fast artificial immune systems D Corus, PS Oliveto, D Yazdani International Conference on Parallel Problem Solving from Nature, 67-78, 2018 | 16 | 2018 |
| On easiest functions for mutation operators in bio-inspired optimisation D Corus, J He, T Jansen, PS Oliveto, D Sudholt, C Zarges Algorithmica 78 (2), 714-740, 2017 | 13 | 2017 |
| A parameterised complexity analysis of bi-level optimisation with evolutionary algorithms D Corus, PK Lehre, F Neumann, M Pourhassan Evolutionary computation 24 (1), 183-203, 2016 | 13 | 2016 |
| On easiest functions for somatic contiguous hypermutations and standard bit mutations D Corus, J He, T Jansen, PS Oliveto, D Sudholt, C Zarges Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 11 | 2015 |
| On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms D Corus, PS Oliveto Algorithmica 82 (12), 3676-3706, 2020 | 10 | 2020 |
| Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem D Corus, PS Oliveto, D Yazdani Artificial Intelligence 274, 180-196, 2019 | 10 | 2019 |
| Artificial immune systems can find arbitrarily good approximations for the NP-hard partition problem D Corus, PS Oliveto, D Yazdani International Conference on Parallel Problem Solving from Nature, 16-28, 2018 | 9 | 2018 |
| The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation D Corus, PK Lehre, F Neumann Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013 | 7 | 2013 |
| When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms D Corus, PS Oliveto, D Yazdani Theoretical Computer Science 832, 166-185, 2020 | 6 | 2020 |
| On inversely proportional hypermutations with mutation potential D Corus, PS Oliveto, D Yazdani Proceedings of the Genetic and Evolutionary Computation Conference, 215-223, 2019 | 4 | 2019 |
| Theory Driven Design of Efficient Genetic Algorithms for a Classical Graph Problem D Corus, PK Lehre Recent Developments in Metaheuristics, 125-140, 2018 | 2 | 2018 |
| Standard steady state genetic algorithms can hillclimb faster than evolutionary algorithms using standard bit mutation D Corus, PS Oliveto Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018 | 1 | 2018 |
| On steady-state evolutionary algorithms and selective pressure: Why inverse rank-Based allocation of reproductive trials is best D Corus, A Lissovoi, P Oliveto, C Witt ACM Transactions on Evolutionary Learning and Optimization, 2020 | | 2020 |
| Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation D Corus, PS Oliveto, D Yazdani arXiv preprint arXiv:2009.00990, 2020 | | 2020 |
| Runtime analysis of evolutionary algorithms with complex fitness evaluation mechanisms D Corus University of Nottingham, 2018 | | 2018 |
Pietro S. OlivetoSenior Lecturer, EPSRC Early Career Fellow, The University of Sheffield, UKVerified email at sheffield.ac.uk
