Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation to generate test problems with academic purposes. This method changes the mapping between solutions and objective values, but preserves the structure of the fitness landscape. In this work, the rotation of the landscape in the combinatorial domain is theoretically analysed using concepts of discrete mathematics. Certainly, the preservation of the neighbourhood relationship between the solutions and the structure of the landscape are studied in detail. Based on the theoretical insights obtained, landscape rotation has been employed as a strategy to escape from local optima when local search algorithms get stuck. Conducted experiments confirm th...
Many combinatorial optimization problems are hard to solve and in many cases, exact approaches are i...
Using the recently proposed model of combinatorial landscapes: local optima networks, we study the d...
International audienceBlack-box optimization of a previously unknown problem can often prove to be a...
Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation t...
Combinatorial optimization involves finding an optimal solution in a finite set of options; many eve...
Local Optima Networks are models proposed to understand the structure and properties of combinatoria...
In a series of papers we introduced a novel model for combinatorial landscapes that we called Local ...
International audienceOne of the most commonly-used metaphors to describe the process of heuristic s...
This paper carries out a comparison of the fitness landscape for four classic optimization problems:...
International audienceThe proper setting of algorithm parameters is a well-known issue that gave ris...
In order to understand the structure of a problem we need to measure some features of the problem. S...
Abstract. Using the recently proposed model of combinatorial landscapes: lo-cal optima networks, we ...
A number of local search based algorithms have been designed to escape from the local optima, such a...
Abstract. A significant challenge in nature-inspired algorithmics is the identification of specific ...
A significant challenge in nature-inspired algorithmics is the identification of specific characteri...
Many combinatorial optimization problems are hard to solve and in many cases, exact approaches are i...
Using the recently proposed model of combinatorial landscapes: local optima networks, we study the d...
International audienceBlack-box optimization of a previously unknown problem can often prove to be a...
Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation t...
Combinatorial optimization involves finding an optimal solution in a finite set of options; many eve...
Local Optima Networks are models proposed to understand the structure and properties of combinatoria...
In a series of papers we introduced a novel model for combinatorial landscapes that we called Local ...
International audienceOne of the most commonly-used metaphors to describe the process of heuristic s...
This paper carries out a comparison of the fitness landscape for four classic optimization problems:...
International audienceThe proper setting of algorithm parameters is a well-known issue that gave ris...
In order to understand the structure of a problem we need to measure some features of the problem. S...
Abstract. Using the recently proposed model of combinatorial landscapes: lo-cal optima networks, we ...
A number of local search based algorithms have been designed to escape from the local optima, such a...
Abstract. A significant challenge in nature-inspired algorithmics is the identification of specific ...
A significant challenge in nature-inspired algorithmics is the identification of specific characteri...
Many combinatorial optimization problems are hard to solve and in many cases, exact approaches are i...
Using the recently proposed model of combinatorial landscapes: local optima networks, we study the d...
International audienceBlack-box optimization of a previously unknown problem can often prove to be a...