Evolutionary computation (EC) has gained increasing popularity in dealing with permutation-
based combinatorial optimization problems (PCOPs). Traditionally, EC focuses on solving a …

The Mallows (MM) and the Generalized Mallows (GMM) probability models have
demonstrated their validity in the framework of Estimation of distribution algorithms (EDAs) …

Faced with the challenge of solving hard optimization problems that abound in the real
world, classical methods often encounter serious difficulties. Important applications in …

Neural Combinatorial Optimization has emerged as a new paradigm in the optimization
area. It attempts to solve optimization problems by means of neural networks and …

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization
problems (COP). Traditional EC methods can only solve a single task in a single run, while …

Condorcet consistent rules were originally developed for preference aggregation in the
theory of social choice. Nowadays these rules are applied in a variety of fields such as …

Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of
problems using Neural Network models and Reinforcement Learning. Recently, its good …

The optimal bucket order problem consists in obtaining a complete consensus ranking (ties
are allowed) from a matrix of preferences (possibly obtained from a database of rankings). In …

It is an old claim that, in order to design a (meta) heuristic algorithm for solving a given
optimization problem, algorithm designers need first to gain a deep insight into the structure …

Abstract The Linear Ordering Problem (LOP) is a very popular NP-hard combinatorial
optimization problem with many practical applications that may require the use of large …