9 citations · 15 across the 5 of their papers we have counts for
5 papers
(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…
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,…
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…
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…