Trees are a canonical structure for representing evolutionary histories. Many popular criteria
used to infer optimal trees are computationally hard, and the number of possible tree shapes …

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The combination and …

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for
combinatorial optimization. It is a multi‐start or iterative process, in which each GRASP …

Designing generic problem solvers that perform well across a diverse set of problems is a
challenging task. In this work, we propose a hyper-heuristic framework to automatically …

Introduction Discrete optimization concerns in essence the search for a “best” configuration
(optimal solution) among a set of finite candidate configurations according to a particular …

In this paper, we present an in-depth analysis of neighborhood relations for local search
algorithms. Using a curriculum-based course timetabling problem as a case study, we …

Abstract GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart
metaheuristic for producing good-quality solutions of combinatorial optimization problems …

TreeSearch is an R package for phylogenetic analysis, optimized for morphological
datasets. Tree search may be conducted using under equal or implied step weights with an …

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The diversity in the operator …

Meta-heuristics are generic search methods that are used to solve challenging
combinatorial problems. We describe these methods and highlight their common features …