Recent systems applying Machine Learning (ML) to solve the Traveling Salesman Problem
(TSP) exhibit issues when they try to scale up to real case scenarios with several hundred …

Evolutionary algorithms solve problems by simulating the evolution of a population of
candidate solutions. We focus on evolving permutations for ordering problems such as the …

Machine Learning techniques have been applied in many contexts with great success. In
this survey, we focus on their applications in the Combinatorial Optimization (CO) domain …

The ML-Constructive heuristic is a recently presented method and the first hybrid method
capable of scaling up to real scale traveling salesman problems. It combines machine …

A method is proposed for simulating a group of pilotless aircraft (drones), with preparation of
the initial data for machine learning algorithms and optimal route determination (the …

The paper discusses an enhancement to a recently presented supervised learning algorithm
to solve the Maximum Independent Set problem. In particular, it is shown that the algorithm …

Abstract Stochastic Optimization Problems take uncertainty into account. For this reason they
are in general more realistic than deterministic ones, meanwhile, more difficult to solve. The …