This paper investigates the influence of adaptive walks heuristics within local searches, by
studying to what extent a wiser choice among improving neighbors influences the expected …

Hill-climbing constitutes one of the simplest way to produce approximate solutions of a
combinatorial optimization problem, and is a central component of most advanced …

In local search algorithms, the pivoting rule determines which neighboring solution to select
and thus strongly influences the behavior of the algorithm and its capacity to sample good …

In this work, we study partial neighborhood local search (PNLS) techniques, which consist of
adaptive walks where moves are chosen in a random subset of the current solution …

Solving an optimization problem with local search algorithms consists of evolving a solution
by means of an evaluation function, which is usually directly derived from the objective …

In this paper, we aim at evaluating the impact of the starting point of a basic local search
based on the first improvement strategy. We define the coverage rate of a configuration as …

In this paper, we consider the problem of finding perfectly balanced Boolean functions with
high non-linearity values. Such functions have extensive applications in domains such as …

Local search algorithms consist in evolving a solution guided by a fitness function, which is
usually directly derived from the objective function of the problem. Solving difficulties appear …

Many combinatorial optimization problem solvers are based on stochastic local search
algorithms, which mainly differ by their move selection strategies, also called pivoting rules …

L'optimisation boîte grise se distingue de l'optimisation boîte noire par le fait que des
informations soient disponibles sur la structure du problème que l'on souhaite résoudre. Ces …