Distance-based exponential probability models on constrained combinatorial optimization problems

Estimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained type problems. The great majority of works in the literature implement external repairing or penalty schemes, or use ad-hoc sampling methods in order to avoid unfeasible solutions. In this work, we present a new way to develop EDAs for this type of problems by implementing distance-based exponential probability models defined exclusively on the set of feasible solutions. In order to illustrate this procedure, we take the 2-partition balanced Graph Partitioning Problem as a case of study, and design efficient learning and sampling methods in order to use these distance-based probability models in EDAs

A note on the Boltzmann distribution and the linear ordering problem

The Boltzmann distribution plays a key role in the field of optimization as it directly connects this field with that of probability. Basically, given a function to optimize, the Boltzmann distribution associated to this function assigns higher probability to the candidate solutions with better quality. Therefore, an efficient sampling of the Boltzmann distribution would turn optimization into an easy task. However, inference tasks on this distribution imply performing operations over an exponential number of terms, which hinders its applicability. As a result, the scientific community has investigated how the structure of objective functions is translated to probabilistic properties in order to simplify the corresponding Boltzmann distribution. In this paper, we elaborate on the properties induced in the Boltzmann distribution associated to permutation-based combinatorial optimization problems. Particularly, we prove that certain characteristics of the linear ordering problem are translated as conditional independence relations to the Boltzmann distribution in the form of L − decomposability

Multi-objectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions

In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this paper, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation prob- lems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their perfor- mance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.TIN2016-78365R IT-609-1

perm mateda: A matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems

Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of solutions, they have been a recurrent topic for the artificial intelligence and operations research community. Recently, among the vast number of metaheuristic algorithms, new advances on estimation of distribution algorithms (EDAs) have shown outstanding performance when solving some permutation problems. These novel EDAs implement distance-based exponential probability models such as the Mallows and Generalized Mallows models. In this paper, we present a Matlab package, perm mateda, for estimation of distribution algorithms on permutation problems, which has been implemented as an extension to the Mateda-2.0 toolbox of EDAs. Particularly, we provide implementations of the Mallows and Generalized Mallows EDAs under the Kendall’s-τ, Cayley, and Ulam distances. In addition, four classical permutation problems have been also implemented: Traveling Salesman Problem, Permutation Flowshop Scheduling Problem, Linear Ordering Problem, and Quadratic Assignment Problem

ANALYTICAL SOLUTION FOR TRANSIENT ONEDIMENSIONAL COUETTE FLOW CONSIDERING CONSTANT AND TIME-DEPENDENT PRESSURE GRADIENTS

This paperaims to determine the velocity profile, in transient state, for a parallel incompressible flow known as Couette flow. The Navier-Stokes equations were applied upon this flow. Analytical solutions, based in Fourier series and integral transforms, were obtained for the one-dimensional transient Couette flow, taking into account constant and time-dependent pressure gradients acting on the fluid since the same instant when the plate starts it´s movement. Taking advantage of the orthogonality and superposition properties solutions were foundfor both considered cases. Considering a time-dependent pressure gradient, it was found a general solution for the Couette flow for a particular time function. It was found that the solution for a time-dependent pressure gradient includes the solutions for a zero pressure gradient and for a constant pressure gradient

Efficient Concept Drift Handling for Batch Android Malware Detection Models

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.Comment: 18 page

VELOCITY PROFILE MODELING FOR NON-ISOTHERMAL FLOWS INSIDE A CIRCULAR TUBE

This research proposes a new method to establish the velocity field and the dimensionless velocity profile for Newtonian and non-Newtonian flows inside a circular tube. Several studies developed regarding different fluid types (such as potency law fluid, Bingham and Herschel-Bulkley, among others) observed that a rational or irrational polynomial was used for the dependent velocity field variable. Thus, a rational polynomial was established as a starting point for this research as the dependent velocity field variable. Dimensionless velocity profiles obtained from the proposed fluid-dynamics model were experimentally compared only with dimensionless velocity profiles for non-isothermal Newtonian flows of glycerol, in cooling as well as heating. On the other hand, it was possible to calculate that RMS errors found using relative dimensionless velocity data obtained from the proposed fluid-dynamics model creates very small errors, which are comparable to RMS errors found using data obtained from application of a numerical method. Finally, the proposed fluid-dynamics model was validated with a dimensionless velocity profile obtained from the flow of a cooling process, resulting in the validity of the proposed model

Drought and Freezing Vulnerability of the Isolated Hybrid Aspen \u3cem\u3ePopulus x smithii\u3c/em\u3e Relative to Its Parental Species, \u3cem\u3eP. tremuloides\u3c/em\u3e and \u3cem\u3eP. grandidentata\u3c/em\u3e

Aim We assessed the vulnerability of an isolated, relictual Pleistocene hybrid aspen population of conservation interest (Populus x. smithii ) and the nearest populations of its parent species (Populus grandidentata and Populus tremuloides ) to springtime post‐bud break freezing and growing season drought stress. Response to these stressors in the three taxa was compared in terms of avoidance and tolerance. Location North American Midwest; USA. Methods Unique genets from the hybrid Niobrara River population and from the two parental populations were propagated in a common garden from rhizome cuttings. We tracked their phenology before and after bud break and measured their vulnerability to freezing (stem electrolyte leakage and leaf chlorophyll fluorescence) and to drought (stem hydraulic conductance, leaf osmotic potential, stomatal pore index, and gas exchange). Results Populus grandidentata was slower to leaf out, showed lower vulnerability to stem freezing and drought‐induced cavitation, but exhibited a lower capacity to tolerate drought stress through leaf resistance traits compared to P. tremuloides . Hybrids were similar to P. grandidentata in their overwintering strategy, exhibiting later bud break, and in their higher resistance to stem freezing damage, but they were more similar to P. tremuloides in their higher vulnerability to drought‐induced cavitation. The hybrids shared various leaf‐level gas exchange traits with both parents. All aspens showed limited loss of leaf photosynthetic function following moderate freezing. Main Conclusions The Niobrara River hybrid population is vulnerable to drought due to its combination of inherited drought avoidance and tolerance traits. As climate changes, P. x smithii will likely suffer from increased drought stress, while being unaffected by frost during warmer springs. The two parental species contrast in their survival mechanisms in response to climatic stress, with P. tremuloides tending toward freezing tolerance but drought avoidance and P. grandidentata tending toward freezing avoidance and drought tolerance

Evolutionary origins of metabolic reprogramming in cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.This work was supported in JPL’s lab by Grant PID2020-118527RB-I00 funded by MCIN/AEI/10.13039/501100011039; Grant PDC2021-121735-I00 funded by MCIN/AEI/10.13039/501100011039 and by the “European Union Next Generation EU/PRTR.”, the Regional Government of Castile and León (CSI234P18 and CSI144P20). SCLl was the recipient of a Ramón y Cajal research contract from the Spanish Ministry of Economy and Competitiveness and was supported by grant RTI2018-094130-B-100 funded by MCIN/AEI/10.13039/501100011039 and by “ERDF A way of making Europe.” RCC and AJN are funded by fellowships from the Spanish Regional Government of Castile and León. NGS is a recipient of an FPU fellowship (MINECO/FEDER). MJPB is funded by grant PID2020-118527RB-I00 funded by MCIN/AEI/10.13039/501100011039. J.C. is partially supported by grant GRS2139/A/20 (Gerencia Regional de Salud de Castilla y León) and by the Instituto de Salud Carlos III (PI18/00587 and PI21/01207), co-financed by FEDER funds, and by the “Programa de Intensificación” of the ISCIII, grant number INT20/00074. We thank Phil Mason for English language support