Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance

The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many approximate methods have been published to tackle this problem, including Estimation of Distribution Algorithms (EDAs). In particular, EDAs have been used to solve permutation problems by introducing distance based exponential models, such as the Mallows Models. In this paper we approximate the QAP with a Hamming distance based kernels of Mallows Models. Based on the benchmark instances, we have observed that our approach is competitive, reaching the best-known solution in $71\%$ of the tested instances, especially on large instances ($n>125$), where it is able to outperform state of the art results in 43 out of 288 instances.TIN2016-78365-R SVP-2014-068574 SEV-2013-032

Statistical assessment of experimental results: a graphical approach for comparing algorithms

Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common. These measures can be modeled as random variables and compared among each other via their expected values or more sophisticated tools such as null hypothesis statistical tests. In this paper, we propose an alternative framework to compare two random variables according to their cumulative distribution functions. First, we introduce a dominance measure for two random variables that quantifies the proportion in which the cumulative distribution function of one of the random variables is greater than the other. Then, we present a graphical method that allows a visual estimation of the proposed dominance measure, the probability that one of the random variables takes lower values than the other, and a comparison of quantiles of the random variables. With illustrative purposes, we re-evaluate the experimentation of an already published work with the proposed methodology and we show that additional conclusions—missed by the rest of the methods—can be inferred. Additionally, a software package is provided as a convenient way of applying the proposed framework.Basque Government through the Research Groups 2019-2021 IT1244-19 Spanish Ministry of Science, Innovation and Universities through PID2019-106453GA-I00/AEI/10.13039/50110001103

Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard problem represents, it has captured the attention of the optimization community for decades. As a result, a large number of algorithms have been proposed to tackle this problem. Among these, exact methods are only able to solve instances of size $n<40$. To overcome this limitation, many metaheuristic methods have been applied to the QAP. In this work, we follow this direction by approaching the QAP through Estimation of Distribution Algorithms (EDAs). Particularly, a non-parametric distance-based exponential probabilistic model is used. Based on the analysis of the characteristics of the QAP, and previous work in the area, we introduce Kernels of Mallows Model under the Hamming distance to the context of EDAs. Conducted experiments point out that the performance of the proposed algorithm in the QAP is superior to (i) the classical EDAs adapted to deal with the QAP, and also (ii) to the specific EDAs proposed in the literature to deal with permutation problems.Severo Ochoa SEV-2013-0323 TIN2016-78365-R PID2019-106453GAI00 SVP-2014-068574 TIN2017-82626-

An adaptive neuroevolution-based hyperheuristic

According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic approaches, at the cost of a more complex design and parameter tuning process. Trying to combine the best of both worlds, the field of hyperheuristics investigates the automatized generation and hybridization of heuristic algorithms. In this paper, we propose a neuroevolution-based hyperheuristic approach. Particularly, we develop a population-based hyperheuristic algorithm that first trains a neural network on an instance of a problem and then uses the trained neural network to control how and which low-level operators are applied to each of the solutions when optimizing different problem instances. The trained neural network maps the state of the optimization process to the operations to be applied to the solutions in the population at each generation.TIN2016-78365R BERC 2014-2017 Research Groups 2013-2018 (IT-609-13)

Implementing the Cumulative Difference Plot in the IOHanalyzer

The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results of such algorithms. In this work, we implement the cumulative difference plot in the IOHanalyzer. The cumulative difference plot is a graphical approach that compares two samples through the first-order stochastic dominance. It improves upon other graphical approaches with the ability to distinguish between a small magnitude of difference and high uncertainty.The data science and artificial intelligence chair for digitalized industry and services, Telecom Paris, Institut Polytechnique de Paris, Research Groups 20192021 (IT1244-19), Spanish Ministry of Economy and Competitiveness, through the research project PID2019-106453GAI00/AEI/10.13039/501100011033

On the fair comparison of optimization algorithms in different machines

An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to be executed in the same machine if they are to use the same resources. Unfortunately, the implementation code of the algorithms is not always available, which means that running the algorithms to be compared in the same machine is not always possible. And even if they are available, some optimization algorithms might be costly to run, such as training large neural-networks in the cloud. In this paper, we consider the following problem: how do we compare the performance of a new optimization algorithm B with a known algorithm A in the literature if we only have the results (the objective values) and the runtime in each instance of algorithm A? Particularly, we present a methodology that enables a statistical analysis of the performance of algorithms executed in different machines. The proposed methodology has two parts. Firstly, we propose a model that, given the runtime of an algorithm in a machine, estimates the runtime of the same algorithm in another machine. This model can be adjusted so that the probability of estimating a runtime longer than what it should be is arbitrarily low. Secondly, we introduce an adaptation of the one-sided sign test that uses a modified p-value and takes into account that probability. Such adaptation avoids increasing the probability of type I error associated with executing algorithms A and B in different machines.PID2019-1064536A-I00 Basque Government through consolidated groups 2019-2021 IT1244-1

Comparing Two Samples Through Stochastic Dominance: A Graphical Approach

Nondeterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common. These measures can be modeled as random variables and compared among each other via their expected values or more sophisticated tools such as null hypothesis statistical tests. In this article, we propose an alternative framework to visually compare two samples according to their estimated cumulative distribution functions. First, we introduce a dominance measure for two random variables that quantifies the proportion in which the cumulative distribution function of one of the random variables stochastically dominates the other one. Then, we present a graphical method that decomposes in quantiles (i) the proposed dominance measure and (ii) the probability that one of the random variables takes lower values than the other. With illustrative purposes, we reevaluate the experimentation of an already published work with the proposed methodology and we show that additional conclusions—missed by the rest of the methods—can be inferred. Additionally, the software package RVCompare was created as a convenient way of applying and experimenting with the proposed framework.PID2019-106453GA-I00 BERC 2018-202

Effect Of Family Environment And Student Learning Motivation To Accounting Student Learning Outcomes Class X Smk 1 Solok South State

The research was conducted at SMK Negeri 1 South Solok the influence of family environment and students motivation towards learning outcomes of accounting students of class X of SMK Negeri 1 South Solok. The purpose of this study is to obtain empirical evidence about the influence of family environment and students motivation towards learning outcomes of accounting students of class X of SMK Negeri 1 South Solok.The method used in this research is descriptive and causative. This research is a saturated sampling or census, which used population sampled as many as 96 students out of a population of 96 students. Family environment data (X1) and motivation (X2) is obtained from the results of the questionnaire or questionnaires while accounting student learning outcomes data (Y) is obtained from the documentation of the value end of the semester. Analysis of the data used in this study is a descriptive analysis and inductive analysis which consists of classical assumption test, multiple regression analysis and hypothesis testing.The results showed that: 1. Family environment and a positive significant effect on learning outcomes of accounting students of class X of SMK Negeri 1 South Solok, 2. Motivation to study significant and positive impact on learning outcomes of accounting students of class X of SMK Negeri 1 South Solok, 3. Family environment and student motivation are jointly significant effect on learning outcomes of accounting students of class X of SMK Negeri 1 South Solok. Based on these results, it is suggested that students, parents, teachers and the school for more attention to the family environment and students motivation in the learning process in an attempt to further optimize the learning outcomes of accounting students of class X of SMK Negeri 1 South Solok. Keywords : Family Environment, Motivation, Learning Outcome

Competencia intercultural en el ámbito de las drogodependencias

En este artículo partimos de una concepción multidimensional o biopsicosocial de las drogodependencias. Dentro de este marco de análisis, abogamos por una mayor visibilización de la dimensión intercultural, ya que es imprescindible para el diseño y desarrollo de procesos de intervención integrales. Proponemos la competencia intercultural como un modelo de trabajo que puede permitir el incremento en las capacidades de instituciones y profesionales, especialmente relevante en el caso de los trabajadores sociales, para abordar eficazmente la dimensión intercultural aludida. Tras una amplia revisión de la literatura científica, hemos definido cinco procesos que pueden contribuir a reforzar la competencia intercultural de una institución y cuatro procesos que pueden contribuir a incrementar la competencia intercultural de un o una profesional. Aunque han sido seleccionados para su aplicación en el ámbito de las drogodependencias, todos estos procesos también pueden servir para mejorar la atención a cualquier otro tipo de persona o grupo culturalmente diverso. This article takes a multidimensional or biopsychosocial conception of drug dependency as its starting point. Within this analytical framework, we advocate making the intercultural dimension more visible, since it is essential for the design and implementation of integral intervention processes. We propose intercultural competence as a working model that can increase the capacities of institutions and professionals —a particularly important consideration in the case of social work— in order to effectively address the aforementioned cultural dimension. After an extensive review of the scientific literature, we have defined five processes that can contribute to strengthening an institution’s intercultural competence and four processes that can do the same for a professional’s intercultural competence. Though selected for application in the area of drug dependencies, all these processes can also prove useful in improving attention to any other kind of culturally diverse group or person

Dietary patterns of Roma population and total population in Spain

Objetivo: La estrategia NAOS (Nutrición, Actividad Física y Prevención de la Obesidad), del Ministerio de Sanidad español, propuso diversas pautas saludables de alimentación. El objetivo de este artículo consiste en analizar el grado de seguimiento de estos patrones nutricionales por parte de la población gitana, en comparación con los referidos al conjunto de la población. Método: Se compararon los datos de la Encuesta Nacional de Salud 2012 y de la Encuesta de Salud a Población Gitana 2014. Como variables independientes se utilizaron el sexo, la edad y la escala socio-ocupacional, aplicando un análisis de regresión logística para valorar su grado de influencia. Resultados: Se observó un menor seguimiento por parte de la población gitana de las pautas de alimentación recomendadas por el Ministerio de Sanidad, con diferencias de más de 20 puntos en algunas de ellas. La edad es la variable con mayor influencia en ambas poblaciones, pero aún con mayor intensidad en la población gitana, mientras que la influencia del sexo no alcanza significación estadística entre las personas gitanas. Los datos de seguimiento de estas pautas de alimentación en la población gitana son más bajos en comparación con cualquiera de los grupos socio-ocupacionales del conjunto de la población. Conclusiones: La población gitana se encuentra en una situación de desigualdad en el grado de mantenimiento de las pautas de alimentación saludable propuestas por la estrategia NAOS. El alejamiento respecto a estos patrones alimenticios es mayor en la población gitana más joven. Objective: The NAOS (Nutrición, Actividad Física y Prevención de la Obesidad) strategy implemented by Spanish Ministry of Health proposes a range of healthy dietary guidelines. The objective of this article is to analyze the degree these nutritional patterns are monitored by the Roma population, compared to the rest of the population, compared to the total Spanish population. Methods: Data from the 2012 National Health Survey and the 2014 Roma Health Survey were compared. Sex, age and socio-occupational scale were used as independent variables, applying a logistic regression analysis to assess their degree of influence. Results: There was less monitoring carried out by the Roma population of the dietary plans recommended by the Ministry of Health and differences of more of 20 percentage points were found in some of them. In both populations, age was the variable with the greatest influence, but even more so in the Roma population, while sex did not show statistical among Roma people. The follow-up data of these dietary patterns in the Roma population were lower in comparison with any of the socio-occupational groups of the rest of the population. Conclusions: The Roma population is in a situation of inequality as regards the levels of adherence of the healthy dietary guidelines proposed by the Spanish NAOS strategy. The distance from these healthy eating habits is even greater among the younger Roma population