In this paper, an analytic review of the recent methodologies tackling the problem of dynamic
allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal …

This chapter reviews applications of Memetic Algorithms in the areas of business analytics
and data science. This approach originates from the need to address optimization problems …

Optimization problems are defined as the functions whereby the target is to find the optimum
state depending on the parameters that have certain limitations. In the field of optimization …

Hybrid metaheuristics have proven to be effective at solving complex real-world problems.
However, designing hybrid metaheuristics is extremely time consuming and requires expert …

The problem of predicting profitability is exceptionally relevant for investors and company
owners. This paper examines the factors affecting firm performance and tests and compares …

The maximum covering location problem (MCLP) is a key problem in facility location, with
many applications and variants. One such variant is the dynamic (or multi-period) MCLP …

The most important component of an Electroencephalogram (EEG) Brain–Computer
Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful …

Algorithm portfolios are multi-algorithmic schemes that combine a number of solvers into a
joint framework for solving global optimization problems. A crucial part of such schemes is …

As a typical large-scale multiobjective optimization problem extracted from real-world
applications, the voltage transformer ratio error estimation (TREE) problem is challenging for …

The maximal covering location problem attempts to locate a limited number of facilities in
order to maximize the coverage over a set of demand nodes. This problem is NP-Hard and it …