Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Oliveto, Pietro S."'
Publikováno v:
Artificial Intelligence 319: 103906 (2023)
Recently it has been proven that simple GP systems can efficiently evolve a conjunction of $n$ variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and perf
Externí odkaz:
http://arxiv.org/abs/2303.07455
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 prove that using uniform parent selection leads to exponential runtimes wi
Externí odkaz:
http://arxiv.org/abs/2103.10394
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 configurator (ParamRLS) for tuning the neighbourhood size $k$ of the RLS$_k$
Externí odkaz:
http://arxiv.org/abs/1904.06230
Recently it has been proved that simple GP systems can efficiently evolve the conjunction of $n$ variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and pe
Externí odkaz:
http://arxiv.org/abs/1903.11936
Artificial Immune Systems (AIS) employing hypermutations with linear static mutation potential have recently been shown to be very effective at escaping local optima of combinatorial optimisation problems at the expense of being slower during the exp
Externí odkaz:
http://arxiv.org/abs/1903.11674
On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms
Autor:
Corus, Dogan, Oliveto, Pietro S.
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 advantages over single-trajectory search heuristics. In this paper we
Externí odkaz:
http://arxiv.org/abs/1903.10976
Publikováno v:
In Artificial Intelligence January 2023 314
Autor:
Lissovoi, Andrei, Oliveto, Pietro S.
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 more traditional evolutionary optimization, the aim of GP is to evolv
Externí odkaz:
http://arxiv.org/abs/1811.04465
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 multimodal optimisation problems. However, this efficiency comes at the expens
Externí odkaz:
http://arxiv.org/abs/1806.00299
Typical artificial immune system (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which evolutionary algorithms (EAs) struggle to escape. Such behaviour has been shown for artif
Externí odkaz:
http://arxiv.org/abs/1806.00300