On the use of preference-based evolutionary multi-objective optimization for solving a credibilistic portfolio selection model

The portfolio selection problem tries to identify the assets to allocate the capital, and the proportion to be devoted to each asset, for maximizing the returns at the minimum risk. By nature, this is a multi-objective optimization problem. In this work, we propose a three-objective model for portfolio selection, in which the uncertainty of the portfolio returns is modelled by means of LR-power fuzzy variables. We consider as criteria the credibilistic expected return (to be maxi- mized), the below-mean absolute semi-deviation as a risk measure (to be minimized), and a loss function which evaluates the credibility of achieving a non-positive return (to be minimized). The uncorrelation among the risk and loss measures concludes that they provide different information. Budget, cardinality, and diversification constraints are considered. To generate non-dominated portfolios fitting the investor' expectations, preference-based evolutionary algorithms are applied. The preferences are given by aspiration values to be attained by the objectives and profiles representing aggressive, cautious, and conservative investors are analysed. The results for data of the IBEX35 show that portfolios improving the preferences are found in the cautious and aggressive cases, while portfolios with objective values as close as possible to the expectations are obtained in the conservative case. In the generation process, the credibilistic loss has played an important role to and diversified portfolios.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

The balance between cognitive and non-cognitive aspects of students’ well-being: a quantitative analysis

The objective of this work is to determine which students’ characteristics (of the 8th grade Spanish students) allow them finding a balance among different dimensions of well-being. Firstly, in order to measure the students’ well-being, we built four synthetic indexes related to anxiety, motivation, belonging to the educational centre and bullying. Then, different models are estimated using as explained variables those indexes and condition on students’ sociodemographic characteristics, use of ICTs, hours of study and a proxy for maturity. Based upon the estimated parameters a multiobjective problem is defined; the problem is solved with multiobjective programing using an achievement scalarazing function. The results allow to characterize the profile of those students who achieve an optimal balance among the different aspects of well-being evaluated, which may be used to design educational policies aimed at improving simultaneously cognitive and non-cognitive measures of students performance.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Reference point approaches in Stochastic Multiobjective Programming.

A new interactive method is proposed for a class of stochastic multiobjective problems, where only the objective functions are random. Several solutions can be generated by this new method, making use of the same preferential information, using the different achievement scalarizing functions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A-GWASF-GA: The New Version of GWASF-GA to Solve Many Objective Problems

A new version of the evolutionary algorithm based on GWASF-GA [1] is proposed in this work. GWASF-GA is an aggregation-based algorithm which uses the Tchebychev metric plus an augmentation term as fitness function and two reference points (the utopian and nadir points) to classify the individuals according to a set of widely-distributed weight vectors. Although this algorithm obtains a good approximation of the Pareto front (PF) for multi-objective optimization problems, this may be more difficult to obtain for many-objective optimization problems due to the fact that the weight vectors used are never updated along the search process. For this reason, we propose a new version of the algorithm, called A-GWASF-GA, in which a dynamic adjustment of the weight vectors is carried out. The main idea is to re-calculate some weight vectors in order to obtain solutions in parts of the PF with a lack of solutions. Firstly, a percentage (p) of the total number of evaluations is performed with the original GWASF-GA [1]. Secondly, during the rest of evaluations (1-p), we re-calculate na times the projection directions determined by a subset of Na weight vectors. The re-calculation process is based on a scattering level, a measure based on the distance of each solution and the solutions around it. According to the scattering level of the generated solutions, we detect the Na weight vectors projecting toward overcrowded areas of the PF and we re-calculate them so that their new projection directions point towards areas of the PF which are not so well approximated. In order to show the effectiveness of A-GWASF-GA, we compare it with NSGA-III [2, 3], MOEA/D [4], MOEA/D-AWA [5] and the original GWASF-GA.To evaluate their performance, we use the IGD metric [6]. The results of the computational experiment demonstrate the good performance of A-GWASF-GA in the novel many-objective optimization benchmark problems considered.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Teachers’ satisfaction as indicator of education system performance

We investigate the potential trade-off between some teacher characteristics, particularly teachers’ satisfaction, and different measures of pupils’ performance, in order to optimize the outputs of the Spanish education system. Our contribution to the existing literature is twofold: on the one hand, we provide estimates on the balance between teachers’ effectiveness and 4th grade students’ performance in Spain, by using recent survey data (TIMSS, 2011); on the other hand we implement a novel methodology which allows optimizing simultaneously a set of indicators on the outputs of the educational system. These analyses provide empirical evidence of the importance of investing in teacher satisfaction as a motivation mechanism for improving national educational achievement.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially supported by the Andalusian Regional Ministry of Innovation, Science and Enterprise (PAI group SEJ-532). We also acknowledge the research funding provided by Andalucía-Tech (Universidad de Malaga),the Research Plan of the University of Malaga (Capacity Building Programme I+D+i of Universities 2014-2015, FEDER Funds) and by the Ministry of Economy and Competitiveness of Spain (Research Project ECO2014-56397-P). Luis Alejandro Lopez-Agudo also acknowledges the research training provided by the “Programa de Doctorado en Economía y Empresa de la Universidad de Malaga”

Un nuevo Algoritmo Evolutivo de Optimizacion Multiobjetivo basado en Preferencias: WASF-GA

IX Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados - MAEB 2013.Para resolver problemas de optimizacion multiobjetivo, los algoritmos evolutivos basados en preferencias incorporan información preferencial en el proceso evolutivo, con el objetivo de encontrar un conjunto de soluciones Pareto optimas acordes a las preferencias del decisor (DM). En este trabajo, se propone un nuevo algoritmo evolutivo basado en preferencias que aproxima la region de la frontera Pareto optima que mejor se ajusta a los valores de aspiracion que el decisor desea alcanzar en cada objetivo. Estos valores constituyen las componentes de lo que se conoce como punto de referencia. El algoritmo hace uso de la conocida función escalarizada de logro de Wierzbicki para clasificar los individuos de cada generacion en diferentes fronteras. Dicha clasificacion se realiza en base a los valores que la función de logro alcanza para el punto de referencia proporcionado, considerando un conjunto de vectores de pesos. Los experimentos realizados sobre conocidos problemas test muestran unos resultados prometedores en cuanto a la calidad de las soluciones obtenidas, en comparacion a otros algoritmos existentes basados en puntos de referencia.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Un Nuevo Algoritmo Evolutivo en Programación Multiobjetivo para Aproximar el Frente Optimo de Pareto.

En este trabajo, presentamos un nuevo algoritmo evolutivo para Programaci´on Multiobjetivo que intenta aproximar todo el frente óptimo de Pareto. En dicho algoritmo, consideramos como función fitness una función escalarizada de logro, que es una extensión de la distancia Tchebychev o minimax. Paralelamente, tomamos dos puntos de referencia, el utopía y el nadir, para clasificar los individuos de cada generación en distintas fronteras. Para asegurar la diversidad de soluciones, se emplea un conjunto de vectores de pesos en la función de logro, que verifican que los vectores formados por las componentes inversas están distribuidos uniformemente. Finalmente, se muestra el funcionamiento del algoritmo propuesto a través de un estudio comparativo con MOEA/D y NSGA-II para varios problemas con tres y cinco funciones objetivo, respecto a la métrica hipervolumen.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

An application of multiobjetive programming to the study of workers' satisfaction in the spanish labour market

In this paper, a multiobjective scheme is used to study the satisfaction levels of the Spanish workers. Data obtained from a panel survey conducted in several European countries are used to build up a multiobjective model, on the basis of a previous statistical and econometric analysis of these data. Then, a Reference Point based method is implemented to determine the profile of the most satisfied worker in Spain nowadays. Finally, a combined Goal Programming – Reference Point approach is used to determine policies than can be carried out in order to increase the workers’ satisfaction levels.Workers’ Satisfaction, Econometric analyses, Multiobjective Programming.

How to reach compromises in life satisfaction: a conflicting and multidimensional task

Recent concerns around the importance of job and life satisfaction on firms’ productivity have been brought to the policy debate in the European Union. Irrespective of the numerous scientific publications that have been studying the main determinants of workers’ life satisfaction, a gap remains on how to grasp compromises between these. The importance of finding such compromises can be particularly relevant, in face of the multiple factors, seldom intertwined and at the same time conflicting, which have a well-known influence on workers’ well-being. Therefore, policymakers should be supported with sound methodological tools to help them shape policies that will further promote workers’ life satisfaction. With this reasoning in mind, we have developed a multiobjective interval programming model through the use of econometric techniques, where four different workers’ life satisfaction determinants (i.e. satisfaction with education, present work, family life and social life) are viewed as objective functions. Furthermore, we have made a separate assessment of the factors that have an influence on life satisfaction for both genders, because of the discrepancies that might arise with this regard. Overall, we were able to conclude that female workers usually have to devote more time to their work than men to get the highest levels of satisfaction regardless of the features under evaluation. Additionally, having one child is sufficient to obtain the highest levels of well-being both for female and male workers. One of the possible implications of these findings is that the existing work-family priorities are clearly not in line with the current falling birth rates all over Europe. Thus, new supportive workplace policies should be in place that could change people’s priorities regarding their work and family life.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech Ministerio de Economía, Industria y Competitividad under research project ECO2017-88883-R PAI group SEJ-532 Fundação para a Ciência e aTecnologia (FCT) under project grant UID/MULTI/00308/201

Coupling distinct MOLP interactive approaches with a novel DEA hybrid model

We propose a modelling framework which allows considering different priorities and individual expansion and contraction scales for distinct types of inputs and outputs, through the Weighted Russell Directional Dis- tance Model (WRDDM). An equivalence model between the WRDDM and the super-ideal point model has also been established, which is then incorporated into several interactive Multiobjective Linear Programming (MOLP) approaches. The use of these diverse interactive methodologies allows obtaining the benchmark Decision Making Units (DMUs) which best suit the decision maker’s (DM) preferences. This feature can be useful since traditional Data Envelopment Analysis (DEA) models tend to completely neglect the DM’s preferences and value judgements in the computation of the DMUs used as a reference of best practices. Therefore, with this tool the DMs have the possibility of translating into the decision-making process man- agement constraints (namely, budgetary) and aspiration levels regarding the inputs and outputs, providing much more realistic support for actual decision-making.This research was partly supported by the Spanish Ministry of Economy and Competitiveness (project ECO2017-88883-R) and by the Fundação para a Ciência e a Tecnologia (FCT) un- der project grant UID/Multi/00308/2020 and the European Regional Development Fund in the framework of COMPETE 2020 Programme within project T4ENERTEC (POCI-01-0145- FEDER-029820). This work has also been partly supported by the Andalusian Regional Min- istry of Economy, Knowledge, Business and University (PAIDI group SEJ-532 and UMA18- FEDERJA-024 also supported by FEDER funding). Carla Oliveira Henriques also acknowledges the training received from the University of Malaga PhD Programme in Economics and Business (Programa de Doctorado en Economía y Empresa de la Universidad de Malaga)