Add to ORKGThere is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of parameters to be configured. Fuzzy logic has been used extensively in control applications and is known for its ability to handle uncertainty. In this study, we investigate the potential of using fuzzy systems to control the parameter settings of a threshold accepting (TA) metaheuristic for improving the overall effectiveness of a cross-domain approach. We have evaluated the performance of various general purpose local search metaheuristics ...
Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a p...
Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test g...
Hyper-heuristics are a class of high-level search methodologies which operate over a search space of...
There is a growing number of studies on general purpose metaheuristics that are directly...
Metaheuristic algorithms are solution approaches to solve optimization problems by repeating some al...
Metaheuristics usually have algorithmic parameters whose initial settings can influence their search...
Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and h...
A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies...
Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new p...
Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its furthe...
Two main concepts are established in the literature for the Parameter Setting Problem (PSP) of metah...
Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metahe...
Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheurist...
Simulated Annealing is a well known local search metaheuristic used for solving computationally hard...
International audienceThis paper presents a method to encapsulate parameters of evolutionary algorit...
Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a p...
Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test g...
Hyper-heuristics are a class of high-level search methodologies which operate over a search space of...
There is a growing number of studies on general purpose metaheuristics that are directly...
Metaheuristic algorithms are solution approaches to solve optimization problems by repeating some al...
Metaheuristics usually have algorithmic parameters whose initial settings can influence their search...
Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and h...
A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies...
Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new p...
Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its furthe...
Two main concepts are established in the literature for the Parameter Setting Problem (PSP) of metah...
Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metahe...
Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheurist...
Simulated Annealing is a well known local search metaheuristic used for solving computationally hard...
International audienceThis paper presents a method to encapsulate parameters of evolutionary algorit...
Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a p...
Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test g...
Hyper-heuristics are a class of high-level search methodologies which operate over a search space of...