Pietro S. Oliveto

ACM Trans. Evol. Learn. Optim., December, 2024

2023

(1+1) genetic programming with functionally complete instruction sets can evolve Boolean conjunctions and disjunctions with arbitrarily small error.

[BibT_eX]

[DOI]

Benjamin Doerr

Artif. Intell., June, 2023

When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not.

[BibT_eX]

[DOI]

Artif. Intell., 2023

Runtime Analysis of Population-based Evolutionary Algorithms - Part I: Steady State EAs.

[BibT_eX]

[DOI]

Pietro Simone Oliveto

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

2022

Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm.

[BibT_eX]

[DOI]

Algorithmica, 2022

On the impact of the performance metric on efficient algorithm configuration.

[BibT_eX]

[DOI]

Artif. Intell., 2022

Runtime analysis of population-based evolutionary algorithms.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021

Rigorous Performance Analysis of Hyper-heuristics.

[BibT_eX]

[DOI]

Proceedings of the Automated Design of Machine Learning and Search Algorithms, 2021

On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best.

[BibT_eX]

[DOI]

ACM Trans. Evol. Learn. Optim., 2021

Fast Immune System-Inspired Hypermutation Operators for Combinatorial Optimization.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2021

Automatic adaptation of hypermutation rates for multimodal optimisation.

[BibT_eX]

[DOI]

Proceedings of the FOGA '21: Foundations of Genetic Algorithms XVI, 2021

2020

Computational Complexity Analysis of Genetic Programming.

[BibT_eX]

[DOI]

Proceedings of the Theory of Evolutionary Computation, 2020

Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2020

Theory of evolutionary computation - Special Issue Editorial.

[BibT_eX]

[DOI]

Andrew M. Sutton

Theor. Comput. Sci., 2020

When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2020

Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes<sup>*</sup>.

[BibT_eX]

[DOI]

Evol. Comput., 2020

Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation.

[BibT_eX]

[DOI]

CoRR, 2020

On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms.

[BibT_eX]

[DOI]

Algorithmica, 2020

Fast Perturbative Algorithm Configurators.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

A tight lower bound on the expected runtime of standard steady state genetic algorithms.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Runtime analysis of population-based evolutionary algorithms: introductory tutorial at GECCO 2020.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Analysis of the performance of algorithm configurators for search heuristics with global mutation operators.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Do sophisticated evolutionary algorithms perform better than simple ones?

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

On the benefits and risks of using fitness sharing for multimodal optimisation.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2019

On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2019

On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?

[BibT_eX]

[DOI]

Algorithmica, 2019

Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem.

[BibT_eX]

[DOI]

Artif. Intell., 2019

How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation.

[BibT_eX]

Proceedings of the 11th International Joint Conference on Computational Intelligence, 2019

Runtime analysis of evolutionary algorithms: basic introduction: introductory tutorial at GECCO 2019.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

On the impact of the cutoff time on the performance of algorithm configurators.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Evolving boolean functions with conjunctions and disjunctions via genetic programming.

[BibT_eX]

[DOI]

Benjamin Doerr

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

On inversely proportional hypermutations with mutation potential.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Theoretical Analysis of Stochastic Search Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Handbook of Heuristics., 2018

Escaping Local Optima Using Crossover With Emergent Diversity.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2018

Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2018

Computational Complexity Analysis of Genetic Programming.

[BibT_eX]

[DOI]

CoRR, 2018

Hyper-heuristics Can Achieve Optimal Performance for Pseudo-Boolean Optimisation.

[BibT_eX]

[DOI]

CoRR, 2018

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism.

[BibT_eX]

[DOI]

Algorithmica, 2018

Workshops at PPSN 2018.

[BibT_eX]

[DOI]

Robin C. Purshouse

Sylvain Cussat-Blanc

Michael G. Epitropakis

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Tutorials at PPSN 2018.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Fast Artificial Immune Systems.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Partition Problem.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

On the runtime analysis of selection hyper-heuristics with adaptive learning periods.

[BibT_eX]

[DOI]

Benjamin Doerr

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Standard steady state genetic algorithms can hillclimb faster than evolutionary algorithms using standard bit mutation.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

2017

Theoretical Analysis of Stochastic Search Algorithms.

[BibT_eX]

[DOI]

CoRR, 2017

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation.

[BibT_eX]

[DOI]

Algorithmica, 2017

When is it beneficial to reject improvements?

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Runtime analysis of population-based evolutionary algorithms: introductory tutorial at GECCO 2017.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

On the runtime analysis of the opt-IA artificial immune system.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016

Escaping Local Optima using Crossover with Emergent or Reinforced Diversity.

[BibT_eX]

[DOI]

CoRR, 2016

Tutorials at PPSN 2016.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.

[BibT_eX]

[DOI]

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

Escaping Local Optima with Diversity Mechanisms and Crossover.

[BibT_eX]

[DOI]

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

On the Analysis of Simple Genetic Programming for Evolving Boolean Functions.

[BibT_eX]

[DOI]

Andrea Mambrini

Proceedings of the Genetic Programming - 19th European Conference, 2016

2015

Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2015

Improved time complexity analysis of the Simple Genetic Algorithm.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2015

Editorial for the Special Issue on Theory of Evolutionary Algorithms 2014.

[BibT_eX]

[DOI]

Andrew M. Sutton

Evol. Comput., 2015

On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

2014

On the runtime analysis of the Simple Genetic Algorithm.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2014

On the Runtime Analysis of Fitness Sharing Mechanisms.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIII, 2014

On the runtime analysis of stochastic ageing mechanisms.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2014

Runtime analysis of evolutionary algorithms: basic introduction.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2014

2013

Improved runtime analysis of the simple genetic algorithm.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Approximating vertex cover using edge-based representations.

[BibT_eX]

[DOI]

Thomas Jansen

Proceedings of the Foundations of Genetic Algorithms XII, 2013

2012

Erratum: Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation.

[BibT_eX]

[DOI]

CoRR, 2012

On the analysis of the simple genetic algorithm.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011

Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation.

[BibT_eX]

[DOI]

Algorithmica, 2011

Evolutionary Algorithms for the Identification of Structural Systems in Earthquake Engineering.

[BibT_eX]

Proceedings of the ECTA and FCTA 2011, 2011

On the Analysis of the Immune-Inspired B-Cell Algorithm for the Vertex Cover Problem.

[BibT_eX]

[DOI]

Thomas Jansen

Proceedings of the Artificial Immune Systems - 10th International Conference, 2011

On the effectiveness of crossover for migration in parallel evolutionary algorithms.

[BibT_eX]

[DOI]

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Runtime Analysis of Evolutionary Algorithms for Discrete Optimization.

[BibT_eX]

[DOI]

Proceedings of the Theory of Randomized Search Heuristics: Foundations and Recent Developments., 2011

2010

Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation.

[BibT_eX]

[DOI]

Stefan Kratsch

Frank Neumann

Pietro Simone Oliveto

Proceedings of the Parallel Problem Solving from Nature, 2010

Ant colony optimization and the minimum cut problem.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2010

Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game.

[BibT_eX]

[DOI]

Arjun Chandra

Pietro Simone Oliveto

Proceedings of the Applications of Evolutionary Computation, 2010

2009

Analysis of the (1+1) -EA for Finding Approximate Solutions to Vertex Cover Problems.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2009

Analysis of Diversity-Preserving Mechanisms for Global Exploration.

[BibT_eX]

[DOI]

Evol. Comput., 2009

Theoretical analysis of fitness-proportional selection: landscapes and efficiency.

[BibT_eX]

[DOI]

Frank Neumann

Proceedings of the Genetic and Evolutionary Computation Conference, 2009

Theoretical analysis of rank-based mutation - combining exploration and exploitation.

[BibT_eX]

[DOI]

Frank Neumann

Proceedings of the IEEE Congress on Evolutionary Computation, 2009

2008

Theoretical analysis of diversity mechanisms for global exploration.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2008

Analysis of population-based evolutionary algorithms for the vertex cover problem.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2008

2007

Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results.

[BibT_eX]

[DOI]

Int. J. Autom. Comput., 2007

On the Convergence of Immune Algorithms.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium on Foundations of Computational Intelligence, 2007

Evolutionary algorithms and the Vertex Cover problem.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2007

2006

Analysis of an evolutionary algorithm with HyperMacromutation and stop at first constructive mutation heuristic for solving trap functions.

[BibT_eX]

[DOI]

Vincenzo Cutello

Giuseppe Nicosia