1,197 research outputs found
MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification
Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods
Metaheuristics for the unit commitment problem : The Constraint Oriented Neighbourhoods search strategy
Tese de mestrado. Faculdade de Engenharia. Universidade do Porto. 199
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension
Scientists must understand what machines do
(systems should not behave like a black box), because in
many cases how they predict is more important than what
they predict. In this work, we propose a new extension of
the fuzzy linguistic grammar and a mainly novel interpretable
linear extension for regression problems, together
with an enhanced new linguistic tree-based evolutionary
multiobjective learning approach. This allows the general
behavior of the data covered, as well as their specific
variability, to be expressed as a single rule. In order to
ensure the highest transparency and accuracy values, this
learning process maximizes two widely accepted semantic
metrics and also minimizes both the number of rules and
the model mean squared error. The results obtained in 23
regression datasets show the effectiveness of the proposed
method by applying statistical tests to the said metrics,
which cover the different aspects of the interpretability of
linguistic fuzzy models. This learning process has obtained
the preservation of high-level semantics and less than 5
rules on average, while it still clearly outperforms some of
the previous state-of-the-art linguistic fuzzy regression
methods for learning interpretable regression linguistic
fuzzy systems, and even to a competitive, pure accuracyoriented
linguistic learning approach. Finally, we analyze a
case study in a real problem related to childhood obesity,
and a real expert carries out the analysis shown.Andalusian Government P18-RT-2248Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities PI20/00711Spanish Government PID2019-107793GB-I00
PID2020-119478GB-I0
A variable neighborhood search simheuristic for project portfolio selection under uncertainty
With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases
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