Items where authors include "Oliveto, P.S."

Article

Lissovoi, A., Oliveto, P.S. and Warwicker, J.A. (2022) When move acceptance selection hyper-heuristics outperform metropolis and elitist evolutionary algorithms and when not. Artificial Intelligence. 103804. ISSN 0004-3702

Oliveto, P.S., Sudholt, D. and Witt, C. (2022) Tight bounds on the expected runtime of a standard steady state genetic algorithm. Algorithmica, 84 (6). pp. 1603-1658. ISSN 0178-4617

Hall, G.T., Oliveto, P.S. and Sudholt, D. (2022) On the impact of the performance metric on efficient algorithm configuration. Artificial Intelligence, 303. 103629. ISSN 0004-3702

Corus, D., Oliveto, P.S. and Yazdani, D. (2021) Fast immune system-inspired hypermutation operators for combinatorial optimization. IEEE Transactions on Evolutionary Computation, 25 (5). pp. 956-970. ISSN 1089-778X

Corus, D. and Oliveto, P.S. (2020) On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms. Algorithmica, 82 (12). pp. 3676-3706. ISSN 0178-4617

Corus, D., Oliveto, P.S. and Yazdani, D. (2020) When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms. Theoretical Computer Science, 832. pp. 166-185. ISSN 0304-3975

Lissovoi, A. and Oliveto, P.S. (2019) On the time and space complexity of genetic programming for evolving Boolean conjunctions. Journal of Artificial Intelligence Research, 66. pp. 655-689. ISSN 1076-9757

Corus, D., Oliveto, P.S. and Yazdani, D. (2019) Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem. Artificial Intelligence, 274. pp. 180-196. ISSN 0004-3702

Oliveto, P.S., Sudholt, D. orcid.org/0000-0001-6020-1646 and Zarges, C. (2019) On the benefits and risks of using fitness sharing for multimodal optimisation. Theoretical Computer Science, 773. pp. 53-70. ISSN 0304-3975

Nallaperuma, S., Oliveto, P.S., Perez Heredia, J. et al. (1 more author) (2019) On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements? Algorithmica, 81 (2). pp. 858-885. ISSN 0178-4617

Corus, D. and Oliveto, P.S. (2018) Standard steady state genetic algorithms can hillclimb faster than mutation-only evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 22 (5). pp. 720-732. ISSN 1089-778X

Dang, D.C., Friedrich, T., Kötzing, T. et al. (5 more authors) (2018) Escaping Local Optima Using Crossover with Emergent Diversity. IEEE Transactions on Evolutionary Computation, 22 (3). pp. 484-497. ISSN 1089-778X

Oliveto, P.S., Paixão, T., Pérez Heredia, J. et al. (2 more authors) (2018) How to escape local optima in black box optimisation: when non-elitism outperforms elitism. Algorithmica, 80 (5). pp. 1604-1633. ISSN 0178-4617

Corus, D., He, J., Jansen, T. et al. (3 more authors) (2017) On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica, 78 (2). pp. 714-740. ISSN 0178-4617

Book Section

Lissovoi, A. and Oliveto, P.S. (2020) Computational complexity analysis of genetic programming. In: Doerr, B. and Neumann, F., (eds.) Theory of Evolutionary Computation: Recent Developments in Discrete Optimization. Natural Computing Series . Springer Nature Switzerland AG , pp. 475-518. ISBN 9783030294137

Lehre, P.K. and Oliveto, P.S. (2018) Theoretical analysis of stochastic search algorithms. In: Martí, R., Panos, P. and Resende, M.G.C., (eds.) Handbook of Heuristics. Springer, Cham . ISBN 9783319071534

Proceedings Paper

Hall, G.T., Oliveto, P.S. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2020) Fast perturbative algorithm configurators. In: Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C. and Emm, M., (eds.) Parallel Problem Solving from Nature – PPSN XVI. International Conference on Parallel Problem Solving from Nature (PPSN 2020), 05-09 Sep 2020, Leiden, Netherlands. Lecture Notes in Computer Science, 12269 . Springer , pp. 19-32. ISBN 9783030581114

Hall, G.T., Oliveto, P.S. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2020) Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. In: Coello Coello, C.A., (ed.) GECCO 2020: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '20: Genetic and Evolutionary Computation Conference, 08-12 Jul 2020, Cancún Mexico (Online). ACM Digital Library , pp. 823-831. ISBN 9781450371285

Oliveto, P.S., Sudholt, D. orcid.org/0000-0001-6020-1646 and Witt, C. (2020) A tight lower bound on the expected runtime of standard steady state genetic algorithms. In: GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. GECCO '20: Genetic and Evolutionary Computation Conference, 08-12 Jul 2020, Online conference. ACM Digital Library , pp. 1323-1331. ISBN 9781450371285

Foster, M., Hughes, M., O'Brien, G. et al. (4 more authors) (2020) Do sophisticated evolutionary algorithms perform better than simple ones? In: GECCO 2020: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2020 : Genetic and Evolutionary Computation Conference, 08-12 Jul 2020, Cancún, Mexico. ACM Digital Library , pp. 184-192. ISBN 9781450371285

Doerr, B., Lissovoi, A. and Oliveto, P.S. (2019) Evolving boolean functions with conjunctions and disjunctions via genetic programming. In: GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference. The Genetic and Evolutionary Computation Conference - GECCO 2019, 13-17 Jul 2019, Prague, Czech Republic. ACM Digital Library . ISBN 9781450361118

Corus, D., Oliveto, P.S. and Yazdani, D. (2019) On inversely proportional hypermutations with mutation potential. In: GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2019, 13-17 Jul 2019, Prague, Czech Republic. ACM , pp. 215-223. ISBN 978-1-4503-6111-8

Corus, D. and Oliveto, P.S. (2019) On the benefits of populations on the exploitation speed of standard steady-state genetic algorithms. In: GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '19, 13-17 Jul 2019, Prague, Czech Republic. ACM , pp. 1452-1460. ISBN 978-1-4503-6111-8

Corus, D., Oliveto, P.S. and Yazdani, D. (2018) Artificial Immune Systems can find arbitrarily good approximations for the NP-Hard partition problem. In: Auger, A., Fonseca, C., Lourenço, N., Machado , P., Paquete, L. and Whitley, D., (eds.) Parallel Problem Solving from Nature – PPSN XV, PT II. PPSN: 15th International Conference on Parallel Problem Solving from Nature, 08-12 Sep 2018, Coimbra, Portugal. Lecture Notes in Computer Science, 11102 . Springer, Cham , pp. 16-28. ISBN 9783319992587

Doerr, B., Lissovoi, A., Oliveto, P.S. et al. (1 more author) (2018) On the runtime analysis of selection hyper-heuristics with adaptive learning periods. In: Aguirre, H.E. and Takadama, K., (eds.) GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '18 Genetic and Evolutionary Computation Conference, 15-19 Jul 2018, Kyoto, Japan. ACM , pp. 1015-1022. ISBN 978-1-4503-5618-3

Lissovoi, A. and Oliveto, P.S. (2018) On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions. In: McIlraith, S.A. and Weinberger, K.Q., (eds.) Thirty-Second AAAI Conference on Artificial Intelligence. Thirty-Second AAAI Conference on Artificial Intelligence, 02-07 Feb 2017, New Orleans, Louisiana, USA. Association for the Advancement of Artificial Intelligence .

Lissovoi, A., Oliveto, P.S. and Warwicker, J.A. (2017) On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation. In: GECCO 2017: Proceedings of the 2017 Genetic and Evolutionary Computation Conference. GECCO '17 The Genetic and Evolutionary Computation Conference, 15-19 Jul 2017, Berlin, Germany. ACM , pp. 849-856. ISBN 9781450349208

Nallaperuma, S., Oliveto, P.S., Perez Heredia, J. et al. (1 more author) (2017) When is it Beneficial to Reject Improvements? In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 17). Genetic and Evolutionary Computation Conference (GECCO 17), 15/07/2017 - 19/07/2017, Berlin. ACM , pp. 1391-1398.

Dang, D., Friedrich, T., Kötzing, T. et al. (5 more authors) (2016) Emergence of Diversity and its Benefits for Crossover in Genetic Algorithms. In: Parallel Problem Solving from Nature – PPSN XIV. Parallel Problem Solving from Nature (PPSN 2016), 17-21 Sep 2016, Edinburgh, UK. Lecture Notes in Computer Science, 9921 . Springer International Publishing .

Sudholt, D. orcid.org/0000-0001-6020-1646, Perez Heredia, J., Paixao, T. et al. (2 more authors) (2016) When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016. Genetic and Evolutionary Computation Conference (GECCO 2016), 20-24 Jul 2016, Denver, Colorado, USA. ACM , pp. 1163-1170. ISBN 978-1-4503-4206-3