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ORCIDPietro S. Oliveto
Pietro Simone Oliveto
Person information
- affiliation: Southern University of Science and Technology, Department of Computer Science and Engineering, Shenzhen, China
- affiliation: University of Sheffield, Department of Computer Science, Sheffield, UK
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2020 – today
- 2025
[c64]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Fast Contiguous Somatic Hypermutations for Single-Objective Optimisation and Multi-Objective Optimisation Via Decomposition. AAAI 2025: 26922-26930- [c63]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) Genetic Programming with Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error. AAAI 2025: 28740 - [c62]Dogan Corus
, Pietro S. Oliveto, Feiyang Zheng:
Hybrid Selection Allows Steady-State Evolutionary Algorithms to Control the Selective Pressure in Multimodal Optimisation. GECCO 2025: 881-889 - [c61]Yuxuan Ma, Pietro S. Oliveto, John Alasdair Warwicker:
Random Gradient Hyper-heuristics Can Learn to Escape Local Optima in Multimodal Optimisation. GECCO 2025: 1622-1630 - 2024
- [j28]Dogan Corus, Pietro S. Oliveto:
On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations. ACM Trans. Evol. Learn. Optim. 4(4): 25:1-25:33 (2024) - 2023
- [j27]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not. Artif. Intell. 314: 103804 (2023) - [j26]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) genetic programming with functionally complete instruction sets can evolve Boolean conjunctions and disjunctions with arbitrarily small error. Artif. Intell. 319: 103906 (2023) - [c60]Per Kristian Lehre, Pietro Simone Oliveto:
Runtime Analysis of Population-based Evolutionary Algorithms - Part I: Steady State EAs. GECCO Companion 2023: 1271-1300 - [i18]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error. CoRR abs/2303.07455 (2023) - 2022
- [j25]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
On the impact of the performance metric on efficient algorithm configuration. Artif. Intell. 303: 103629 (2022) - [j24]Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm. Algorithmica 84(6): 1603-1658 (2022) - [c59]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of population-based evolutionary algorithms. GECCO Companion 2022: 1398-1426 - 2021
- [j23]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Fast Immune System-Inspired Hypermutation Operators for Combinatorial Optimization. IEEE Trans. Evol. Comput. 25(5): 956-970 (2021) - [j22]Dogan Corus, Andrei Lissovoi, Pietro S. Oliveto, Carsten Witt:
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best. ACM Trans. Evol. Learn. Optim. 1(1): 2:1-2:38 (2021) - [c58]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Automatic adaptation of hypermutation rates for multimodal optimisation. FOGA 2021: 4:1-4:12 - [c57]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of evolutionary algorithms: basic introduction. GECCO Companion 2021: 399-425 - [c56]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of population-based evolutionary algorithms. GECCO Companion 2021: 856-880 - [p3]Pietro S. Oliveto:
Rigorous Performance Analysis of Hyper-heuristics. Automated Design of Machine Learning and Search Algorithms 2021: 45-71 - [e1]Steffen Finck, Michael Hellwig, Pietro S. Oliveto:
FOGA '21: Foundations of Genetic Algorithms XVI, Virtual Event, Austria, September 6-8, 2021. ACM 2021, ISBN 978-1-4503-8352-3 [contents] - [i17]Dogan Corus, Andrei Lissovoi, Pietro S. Oliveto, Carsten Witt:
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best. CoRR abs/2103.10394 (2021) - 2020
- [j21]Dogan Corus, Pietro S. Oliveto:
On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms. Algorithmica 82(12): 3676-3706 (2020) - [j20]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes*. Evol. Comput. 28(3): 437-461 (2020) - [j19]Pietro S. Oliveto, Andrew M. Sutton:
Theory of evolutionary computation - Special Issue Editorial. Theor. Comput. Sci. 832: 1-2 (2020) - [j18]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms. Theor. Comput. Sci. 832: 166-185 (2020) - [j17]Pietro S. Oliveto, Anne Auger, Francisco Chicano, Carlos M. Fonseca:
Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation. IEEE Trans. Evol. Comput. 24(6): 993-994 (2020) - [c55]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics. AAAI 2020: 2376-2383 - [c54]Michael Foster, Matthew Hughes, George O. O'Brien, Pietro S. Oliveto, James Pyle, Dirk Sudholt, James Williams:
Do sophisticated evolutionary algorithms perform better than simple ones? GECCO 2020: 184-192 - [c53]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of population-based evolutionary algorithms: introductory tutorial at GECCO 2020. GECCO Companion 2020: 458-494 - [c52]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. GECCO 2020: 823-831 - [c51]Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
A tight lower bound on the expected runtime of standard steady state genetic algorithms. GECCO 2020: 1323-1331 - [c50]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
Fast Perturbative Algorithm Configurators. PPSN (1) 2020: 19-32 - [p2]Andrei Lissovoi, Pietro S. Oliveto:
Computational Complexity Analysis of Genetic Programming. Theory of Evolutionary Computation 2020: 475-518 - [i16]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators. CoRR abs/2004.04519 (2020) - [i15]George T. Hall, Pietro Simone Oliveto, Dirk Sudholt:
Fast Perturbative Algorithm Configurators. CoRR abs/2007.03336 (2020) - [i14]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation. CoRR abs/2009.00990 (2020)
2010 – 2019
- 2019
- [j16]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem. Artif. Intell. 274: 180-196 (2019) - [j15]Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, Dirk Sudholt:
On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements? Algorithmica 81(2): 858-885 (2019) - [j14]Andrei Lissovoi, Pietro S. Oliveto:
On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions. J. Artif. Intell. Res. 66: 655-689 (2019) - [j13]Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On the benefits and risks of using fitness sharing for multimodal optimisation. Theor. Comput. Sci. 773: 53-70 (2019) - [c49]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation. AAAI 2019: 2322-2329 - [c48]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
On inversely proportional hypermutations with mutation potential. GECCO 2019: 215-223 - [c47]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of evolutionary algorithms: basic introduction: introductory tutorial at GECCO 2019. GECCO (Companion) 2019: 662-693 - [c46]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
On the impact of the cutoff time on the performance of algorithm configurators. GECCO 2019: 907-915 - [c45]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
Evolving boolean functions with conjunctions and disjunctions via genetic programming. GECCO 2019: 1003-1011 - [c44]Dogan Corus, Pietro S. Oliveto:
On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms. GECCO 2019: 1452-1460 - [c43]Pietro S. Oliveto:
How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation. IJCCI 2019: 5 - [i13]Dogan Corus, Pietro S. Oliveto:
On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms. CoRR abs/1903.10976 (2019) - [i12]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
On Inversely Proportional Hypermutations with Mutation Potential. CoRR abs/1903.11674 (2019) - [i11]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming. CoRR abs/1903.11936 (2019) - [i10]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
On the Impact of the Cutoff Time on the Performance of Algorithm Configurators. CoRR abs/1904.06230 (2019) - 2018
- [j12]Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism. Algorithmica 80(5): 1604-1633 (2018) - [j11]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Escaping Local Optima Using Crossover With Emergent Diversity. IEEE Trans. Evol. Comput. 22(3): 484-497 (2018) - [j10]Dogan Corus, Pietro S. Oliveto:
Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms. IEEE Trans. Evol. Comput. 22(5): 720-732 (2018) - [c42]Andrei Lissovoi, Pietro S. Oliveto:
On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions. AAAI 2018: 1363-1370 - [c41]Dogan Corus, Pietro S. Oliveto:
Standard steady state genetic algorithms can hillclimb faster than evolutionary algorithms using standard bit mutation. GECCO (Companion) 2018: 11-12 - [c40]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of evolutionary algorithms: basic introduction. GECCO (Companion) 2018: 448-468 - [c39]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
On the runtime analysis of selection hyper-heuristics with adaptive learning periods. GECCO 2018: 1015-1022 - [c38]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Partition Problem. PPSN (2) 2018: 16-28 - [c37]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Fast Artificial Immune Systems. PPSN (2) 2018: 67-78 - [c36]Gisele Lobo Pappa, Michael T. M. Emmerich, Ana L. C. Bazzan, Will N. Browne, Kalyanmoy Deb, Carola Doerr, Marko Durasevic, Michael G. Epitropakis, Saemundur O. Haraldsson, Domagoj Jakobovic, Pascal Kerschke, Krzysztof Krawiec, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang:
Tutorials at PPSN 2018. PPSN (2) 2018: 477-489 - [c35]Robin C. Purshouse, Christine Zarges, Sylvain Cussat-Blanc, Michael G. Epitropakis, Marcus Gallagher, Thomas Jansen, Pascal Kerschke, Xiaodong Li, Fernando G. Lobo, Julian F. Miller, Pietro S. Oliveto, Mike Preuss, Giovanni Squillero, Alberto Paolo Tonda, Markus Wagner, Thomas Weise, Dennis Wilson, Borys Wróbel, Ales Zamuda:
Workshops at PPSN 2018. PPSN (2) 2018: 490-497 - [r1]Per Kristian Lehre, Pietro S. Oliveto:
Theoretical Analysis of Stochastic Search Algorithms. Handbook of Heuristics 2018: 849-884 - [i9]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
Hyper-heuristics Can Achieve Optimal Performance for Pseudo-Boolean Optimisation. CoRR abs/1801.07546 (2018) - [i8]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
When Hypermutations and Ageing Enable Artificial Immune Systems to Outperform Evolutionary Algorithms. CoRR abs/1804.01314 (2018) - [i7]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Fast Artificial Immune Systems. CoRR abs/1806.00299 (2018) - [i6]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Partition Problem. CoRR abs/1806.00300 (2018) - [i5]Andrei Lissovoi, Pietro S. Oliveto:
Computational Complexity Analysis of Genetic Programming. CoRR abs/1811.04465 (2018) - 2017
- [j9]Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica 78(2): 714-740 (2017) - [c34]Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
On the runtime analysis of the opt-IA artificial immune system. GECCO 2017: 83-90 - [c33]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of population-based evolutionary algorithms: introductory tutorial at GECCO 2017. GECCO (Companion) 2017: 414-434 - [c32]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation. GECCO 2017: 849-856 - [c31]Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, Dirk Sudholt:
When is it beneficial to reject improvements? GECCO 2017: 1391-1398 - [i4]Dogan Corus, Pietro S. Oliveto:
Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms. CoRR abs/1708.01571 (2017) - [i3]Per Kristian Lehre, Pietro S. Oliveto:
Theoretical Analysis of Stochastic Search Algorithms. CoRR abs/1709.00890 (2017) - 2016
- [c30]Andrea Mambrini, Pietro S. Oliveto:
On the Analysis of Simple Genetic Programming for Evolving Boolean Functions. EuroGP 2016: 99-114 - [c29]Per Kristian Lehre, Pietro S. Oliveto:
Runtime Analysis of Population-based Evolutionary Algorithms. GECCO (Companion) 2016: 435-462 - [c28]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Escaping Local Optima with Diversity Mechanisms and Crossover. GECCO 2016: 645-652 - [c27]Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys. GECCO 2016: 1163-1170 - [c26]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms. PPSN 2016: 890-900 - [c25]Carola Doerr, Nicolas Bredèche, Enrique Alba, Thomas Bartz-Beielstein, Dimo Brockhoff, Benjamin Doerr, Gusz Eiben, Michael G. Epitropakis, Carlos M. Fonseca, Andreia P. Guerreiro, Evert Haasdijk, Jacqueline Heinerman, Julien Hubert, Per Kristian Lehre, Luigi Malagò, Juan Julián Merelo Guervós, Julian Francis Miller, Boris Naujoks, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Patricia Ryser-Welch, Giovanni Squillero, Jörg Stork, Dirk Sudholt, Alberto Paolo Tonda, L. Darrell Whitley, Martin Zaefferer:
Tutorials at PPSN 2016. PPSN 2016: 1012-1022 - [i2]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity. CoRR abs/1608.03123 (2016) - 2015
- [j8]Pietro S. Oliveto, Andrew M. Sutton:
Editorial for the Special Issue on Theory of Evolutionary Algorithms 2014. Evol. Comput. 23(4): 509-511 (2015) - [j7]Pietro S. Oliveto, Christine Zarges:
Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change. Theor. Comput. Sci. 561: 37-56 (2015) - [j6]Pietro S. Oliveto, Carsten Witt:
Improved time complexity analysis of the Simple Genetic Algorithm. Theor. Comput. Sci. 605: 21-41 (2015) - [c24]Per Kristian Lehre, Pietro S. Oliveto:
Runtime Analysis of Evolutionary Algorithms: Basic Introduction. GECCO (Companion) 2015: 121-136 - [c23]Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations. GECCO 2015: 1399-1406 - 2014
- [j5]Pietro S. Oliveto, Carsten Witt:
On the runtime analysis of the Simple Genetic Algorithm. Theor. Comput. Sci. 545: 2-19 (2014) - [c22]Pietro S. Oliveto, Dirk Sudholt:
On the runtime analysis of stochastic ageing mechanisms. GECCO 2014: 113-120 - [c21]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of evolutionary algorithms: basic introduction. GECCO (Companion) 2014: 459-486 - [c20]Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On the Runtime Analysis of Fitness Sharing Mechanisms. PPSN 2014: 932-941 - 2013
- [c19]Thomas Jansen, Pietro S. Oliveto, Christine Zarges:
Approximating vertex cover using edge-based representations. FOGA 2013: 87-96 - [c18]Per Kristian Lehre, Pietro S. Oliveto:
Runtime analysis of evolutionary algorithms: basic introduction. GECCO (Companion) 2013: 469-498 - [c17]Pietro S. Oliveto, Christine Zarges:
Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change. GECCO 2013: 837-844 - [c16]Pietro S. Oliveto, Carsten Witt:
Improved runtime analysis of the simple genetic algorithm. GECCO 2013: 1621-1628 - 2012
- [c15]Pietro S. Oliveto, Carsten Witt:
On the analysis of the simple genetic algorithm. GECCO 2012: 1341-1348 - [i1]Pietro S. Oliveto, Carsten Witt:
Erratum: Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation. CoRR abs/1211.7184 (2012) - 2011
- [j4]Pietro S. Oliveto, Carsten Witt:
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation. Algorithmica 59(3): 369-386 (2011) - [c14]Frank Neumann, Pietro S. Oliveto, Günter Rudolph, Dirk Sudholt:
On the effectiveness of crossover for migration in parallel evolutionary algorithms. GECCO 2011: 1587-1594 - [c13]Thomas Jansen, Pietro S. Oliveto, Christine Zarges:
On the Analysis of the Immune-Inspired B-Cell Algorithm for the Vertex Cover Problem. ICARIS 2011: 117-131 - [c12]Anastasia Athanasiou, Matteo De Felice, Giuseppe Oliveto, Pietro S. Oliveto:
Evolutionary Algorithms for the Identification of Structural Systems in Earthquake Engineering. IJCCI (ECTA-FCTA) 2011: 52-62 - [p1]Pietro S. Oliveto, Xin Yao:
Runtime Analysis of Evolutionary Algorithms for Discrete Optimization. Theory of Randomized Search Heuristics 2011: 21-52 - 2010
- [c11]Arjun Chandra, Pietro Simone Oliveto, Xin Yao:
Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game. EvoApplications (1) 2010: 61-70 - [c10]Timo Kötzing, Per Kristian Lehre, Frank Neumann, Pietro S. Oliveto:
Ant colony optimization and the minimum cut problem. GECCO 2010: 1393-1400 - [c9]Stefan Kratsch, Per Kristian Lehre, Frank Neumann, Pietro Simone Oliveto:
Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation. PPSN (1) 2010: 204-213
2000 – 2009
- 2009
- [j3]Tobias Friedrich, Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
Analysis of Diversity-Preserving Mechanisms for Global Exploration. Evol. Comput. 17(4): 455-476 (2009) - [j2]Pietro S. Oliveto, Jun He, Xin Yao:
Analysis of the (1+1) -EA for Finding Approximate Solutions to Vertex Cover Problems. IEEE Trans. Evol. Comput. 13(5): 1006-1029 (2009) - [c8]Pietro S. Oliveto, Per Kristian Lehre, Frank Neumann:
Theoretical analysis of rank-based mutation - combining exploration and exploitation. IEEE Congress on Evolutionary Computation 2009: 1455-1462 - [c7]Frank Neumann, Pietro S. Oliveto, Carsten Witt:
Theoretical analysis of fitness-proportional selection: landscapes and efficiency. GECCO 2009: 835-842 - 2008
- [c6]Pietro S. Oliveto, Jun He, Xin Yao:
Analysis of population-based evolutionary algorithms for the vertex cover problem. IEEE Congress on Evolutionary Computation 2008: 1563-1570 - [c5]Tobias Friedrich, Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
Theoretical analysis of diversity mechanisms for global exploration. GECCO 2008: 945-952 - [c4]Pietro S. Oliveto, Carsten Witt:
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation. PPSN 2008: 82-91 - 2007
- [j1]Pietro S. Oliveto, Jun He, Xin Yao:
Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results. Int. J. Autom. Comput. 4(3): 281-293 (2007) - [c3]Pietro S. Oliveto, Jun He, Xin Yao:
Evolutionary algorithms and the Vertex Cover problem. IEEE Congress on Evolutionary Computation 2007: 1870-1877 - [c2]Vincenzo Cutello, Giuseppe Nicosia, Mario Romeo, Pietro S. Oliveto:
On the Convergence of Immune Algorithms. FOCI 2007: 409-415 - 2006
- [c1]Vincenzo Cutello, Giuseppe Nicosia, Pietro S. Oliveto:
Analysis of an evolutionary algorithm with HyperMacromutation and stop at first constructive mutation heuristic for solving trap functions. SAC 2006: 945-949
Coauthor Index
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