35 citations · 60 across the 18 of their papers we have counts for
20 papers
On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics
Pietro S. Oliveto, Zhenyu Wang, Peizhou Wu +1
Selection Hyper-heuristics (HHs) automate algorithmic design by selecting from a set of low-level heuristics which one to apply at each stage of the optimisation process. Several i…
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
Benjamin Doerr, Pietro S. Oliveto, John Alasdair Warwicker
The Random Gradient hyper-heuristic was recently shown to be able to learn the optimal neighbourhood size when optimizing the LeadingOnes benchmark via the Randomised Local Search…
(1+1) Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error
Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto
Recently it has been proven that simple GP systems can efficiently evolve a conjunction of variables if they are equipped with the minimal required components. In this paper, w…
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best
Dogan Corus, Andrei Lissovoi, Pietro S. Oliveto +1
We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state EAs. For the standard bimodal benchmark function \twomax we rigorously prov…
Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation
D. Corus, P. S. Oliveto, D. Yazdani
Various studies have shown that immune system inspired hypermutation operators can allow artificial immune systems (AIS) to be very efficient at escaping local optima of multimodal…
Fast Perturbative Algorithm Configurators
George T. Hall, Pietro Simone Oliveto, Dirk Sudholt
Recent work has shown that the ParamRLS and ParamILS algorithm configurators can tune some simple randomised search heuristics for standard benchmark functions in linear expected t…
Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators
George T. Hall, Pietro Simone Oliveto, Dirk Sudholt
Recently it has been proved that a simple algorithm configurator called ParamRLS can efficiently identify the optimal neighbourhood size to be used by stochastic local search to op…
On the Impact of the Cutoff Time on the Performance of Algorithm Configurators
George T. Hall, Pietro S. Oliveto, Dirk Sudholt
Algorithm configurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search config…
Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming
Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto
Recently it has been proved that simple GP systems can efficiently evolve the conjunction of variables if they are equipped with the minimal required components. In this paper,…
On Inversely Proportional Hypermutations with Mutation Potential
Dogan Corus, Pietro S. Oliveto, Donya Yazdani
Artificial Immune Systems (AIS) employing hypermutations with linear static mutation potential have recently been shown to be very effective at escaping local optima of combinatori…
On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms
Dogan Corus, Pietro S. Oliveto
It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such…
Computational Complexity Analysis of Genetic Programming
Andrei Lissovoi, Pietro S. Oliveto
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in…
Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem
Dogan Corus, Pietro S. Oliveto, Donya Yazdani
Typical artificial immune system (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which evolutionary algori…
Fast Artificial Immune Systems
Dogan Corus, Pietro S. Oliveto, Donya Yazdani
Various studies have shown that characteristic Artificial Immune System (AIS) operators such as hypermutations and ageing can be very efficient at escaping local optima of multimod…
When Hypermutations and Ageing Enable Artificial Immune Systems to Outperform Evolutionary Algorithms
Dogan Corus, Pietro S. Oliveto, Donya Yazdani
We present a time complexity analysis of the Opt-IA artificial immune system (AIS). We first highlight the power and limitations of its distinguishing operators (i.e., hypermutatio…
Simple Hyper-heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes
Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker
Selection HHs are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechani…
Theoretical Analysis of Stochastic Search Algorithms
Per Kristian Lehre, Pietro S. Oliveto
Theoretical analyses of stochastic search algorithms, albeit few, have always existed since these algorithms became popular. Starting in the nineties a systematic approach to analy…
Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms
Dogan Corus, Pietro S. Oliveto
Explaining to what extent the real power of genetic algorithms lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in ev…
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing +5
Population diversity is essential for avoiding premature convergence in Genetic Algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges i…
Erratum: Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Pietro S. Oliveto, Carsten Witt
This erratum points out an error in the simplified drift theorem (SDT) [Algorithmica 59(3), 369-386, 2011]. It is also shown that a minor modification of one of its conditions is s…