Ciencias Exactas y Ciencias de la Salud

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An indicator-based evolutionary algorithm for the numerical treatment of equality constrained multi-objective optimisation problems
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-07-01) Llano García, Jesús Leopoldo; LLANO GARCIA, JESUS LEOPOLDO; 829049; Monroy, Raúl; lagdtorre; Coello Coello, Carlos A.; Amaya Contreras, Ivan Mauricio; Ortiz Bayliss, José Carlos; School of Engineering and Sciences; Campus Estado de México; Sosa Hernández, Víctor Adrián
In many applications, especially those of the real world, we find problems that require for several conflicting objectives to be optimised simultaneously; moreover, these problems may require the consideration of limitations that restrict the space of decisions. These problems arise in the scope of Constrained Optimisation that needs for optimal solutions to follow a set of equality and inequality constraints to be considered valid. While Evolutionary approaches have proven themselves a useful tool for tackling Multi-objective Optimisation Problems (MOPs), they are incapable of accurately approximate the solution when considering Equality Constraints as part of the problem. At the same time, many state-of-the- art algorithms try to incorporate ways to handle Equality Constrained MOPs (ECMOPs) little to none, take into consideration the usage of performance indicators as means for solving this kind of problems. Here, we designed and implemented an EMOA for tackling Equality Constrained MOPs (EC- MOPs). Using a performance indicator as a density estimator, based on an artificially con- structed Reference set that closely resembles the feasible area of a particular ECMOP, the algorithm was able to find Pareto-optimal solutions that both lie within the feasible region and improve the quality of the final approximation. We make an empirical study of our proposed algorithm, testing its capabilities over a set of benchmarking functions composed of bi and three-objective optimisation problems, each with one equality constraint. To give validity to this project, we compare the obtained results against those obtained by two state-of-the-art algorithms. To quantify and compare the performance of each algorithm, we calculated the average Hausdorff distance (∆p) using the actual Pareto front of the benchmark problems, and calculated the ratio of feasible solutions within the final population. The obtained results over the problem set demonstrate that it is possible to approximate the Pareto front of a given ECMOP using only an evolutionary algorithm. We obtain this candidate solution by approximating the shape of the front using an artificially constructed set, which takes into account the information of the constraints to modify the shape. This whole process required no gradient information, preserving the advantages of applying an evolutionary approach to the problems.
Visualization and machine learning techniques to support web traffic analysis
(Instituto Tecnológico y de Estudios Superiores de Monterrey) Gómez-Herrera, Fernando; Monroy, Raúl; Campus Estado de México; Campus Estado de México; Campus Estado de México; Monroy, Raúl
Web Analytics (WA) services are one of the main tools that marketing experts use to measure the success of an online business. Thus, it is extremely important to have tools that support WA analysis. Nevertheless, we observed that there has not been much change in how services display traffic reports. Regarding the trustworthiness of the information, Web Analytics Services (WAS) are facing the problem that more than half of Internet traffic is Non-Human Traffic (NHT). Misleading online reports and marketing budget could be wasted because of that. Some research has been done, yet, most of the work involves intrusive methods and do not take advantage of information provided by current WAS. In the present work, we provide tools that can help the marketing expert to get better reports, to have useful visualizations, and to ensure the trustworthiness of the traffic. First, we propose a new Visualization Tool. It helps to show the website performance in terms of a preferred metric and enable us to identify potential online strategies upon that. Second, we use Machine Learning Binary Classification (BC) and One-Class Classification (OCC) to get more reliable information by identifying NHT and abnormal traffic. Then, marketing analysts could contrast NHT against their current reports. Third, we show how Pattern Extraction algorithms (like PBC4cip's miner) could help to conduct traffic analysis (once visitor segmentation is done), and to propose new strategies that may improve the online business. Later on, the patterns can be used in the Visualization Tool to analyze the traffic in detail. We confirmed the usefulness of the Visualization Tool by using it to analyze bot traffic we generated. NHT traffic shared a very similar linear navigation path, contrasted with the more complex human path. Furthermore, BC and OCC (BaggingTPMiner) worked successfully in the detection of well-known bots and abnormal traffic. We achieved a ROC AUC of 0.844 and 0.982 for each approach, respectively.

Ciencias Exactas y Ciencias de la Salud

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