Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is
to determine the best approach for solving a problem at its current state. This way, every time …

The set cover problem, belongs to the branch of combinatorial optimization problems, whose
complexity is exponential theoretically established as NP-complex problems. Consists in …

This work presents a study of a new binary coded firefly algorithm. The firefly algorithm is a
novel nature-inspired metaheuristic, inspired by the social behavior of fireflies, which is …

Many of the works conducted on algorithm selection strategies—methods that choose a
suitable solving method for a particular problem—start from scratch since only a few …

Selection hyper-heuristics are a technology for optimization in which a high-level
mechanism controls low-level heuristics, so as to be capable of solving a wide range of …

In this paper, we present a new Artificial Bee Colony algorithm to solve the non-unicost Set
Covering Problem. The Artificial Bee Colony algorithm is a recent metaheuristic technique …

The Sudoku is a famous logic‐placement game, originally popularized in Japan and today
widely employed as pastime and as testbed for search algorithms. The classic Sudoku …

Constraint Programming is a powerful paradigm which allows the resolution of many
complex problems, such as scheduling, planning, and configuration. These problems are …

Solving constraint satisfaction problems via constraint programming involves the exploration
of a search tree where the potential solutions are distributed. The exploration phase is …

The nurse rostering problem consists in generating a configuration of daily schedules for a
set of nurses satisfying a set of constraints. The problem is known to be computationally …