Soft Computing: Theory Innovations and Problem-Solving Benefits

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    Introduction to the Minitrack on Soft Computing: Theory Innovations and Problem-Solving Benefits
    ( 2023-01-03) Pérez, Ignacio ; Herrera-Viedma, Enrique ; Cabrerizo, Francisco
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    FuzzyIoT - Platform for Descriptive Analysis of Sensor Data Stream
    ( 2023-01-03) Martínez Mimbrera, Francisco Jesús ; Montoro-Lendínez, Alicia ; López Ruiz, José Luis ; Damas Hermoso, Miguel ; Espinilla, Macarena
    Data generated by sensors are often inaccurate due to calibrations, battery failure or network transmission errors among other issues. Fuzzy protoforms have been demonstrated as a successful tool for modeling data with uncertainty by providing, in addition, linguistic descriptions of the data that offers more relevant and sometimes hidden information. For this purpose, the data are modeled by fuzzy sets whose degree of truth in fuzzy sets is defined by membership functions. In this contribution,a software tool called Fuzzy IoT is presented for the descriptive analysis of sensor data streams through fuzzy protoforms. This software generate linguistic descriptions through the definition of ad hoc protoforms of data exported by different file of sensor data through the previous definition of a set of protoforms. A case study focusing on body temperature is illustrated to show the usefulness of the tool.
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    A Granular-Based Approach to Address Multiplicative Consistency of Reciprocal Preference Relations in Decision-Making
    ( 2023-01-03) Cabrerizo, Francisco ; Kaklauskas, Artūras ; Pérez, Ignacio Javier ; Herrera-Viedma, Enrique
    Decisions, having the possibility to have important consequences on people's lives, are made every day. For this reason, there exists a great need for making good decisions in today's world. Because consistency has been assumed to be a rationality measure, inconsistent judgments are considered to lead to bad decisions. This study aims to introduce a new granular-based approach to deal with consistency, concretely multiplicative consistency, of reciprocal preference relations in decision-making. Firstly, we present a process of an optimal distribution of information granularity maximizing the consistency of the reciprocal preference relation. Secondly, based on it, we develop an interactive procedure for multiplicative consistency improvement with the implication of the decision maker. Several numerical examples are conducted to validate the effectiveness of this granular-based approach.
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    Circuit Testing Based on Fuzzy Sampling with BDD Bases
    ( 2023-01-03) Pinilla, Elena ; Fernandez-Amoros, David ; Heradio, Ruben
    Fuzzy testing of integrated circuits is an established technique. Current approaches generate an approximately uniform random sample from a translation of the circuit to Boolean logic. These approaches have serious scalability issues, which become more pressing with the ever-increasing size of circuits. We propose using a base of binary decision diagrams to sample the translations as a soft computing approach. Uniformity is guaranteed by design and scalability is greatly improved. We test our approach against five other state-of-the-art tools and find our tool to outperform all of them, both in terms of performance and scalability.
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    A Hybrid Job Scheduling Approach on Cloud Computing Environments on the Usage of Heuristics and Metaheuristics Methods
    ( 2023-01-03) Remesh, Abhijith ; Nahhas, Abdulrahman ; Kharitonov, Andrey ; Turowski , Klaus
    The Information Technology Industry has been revolutionized through Cloud Computing by offering dynamic computing services to users through its on-demand provisioning of scalable and virtualized resources over the internet on a pay-per-use measured basis. Performance improvements in task scheduling can have a great impact on the efficiency of cloud computing. This paper proposes a hybrid task scheduling approach which employs the metaheuristic optimization technique, genetic algorithm to produce a certain combination of scheduling heuristics for processing cloud workloads. This approach is developed to optimize the performance metrics namely makespan, average flow time, throughput, and average waiting time. The developed approach is evaluated on the CloudSimPlus simulation framework using large-scale benchmarks against other heuristics in terms of the stated performance metrics. The results indicate that the proposed hybrid approach consistently outperforms the baseline individual heuristics in terms of the stated metrics irrespective of the scale of the workload. It is also observed that the optimization potential tends to increase as the workload scale becomes heavier and optimizing flow time produces complementary effects on the other metrics.
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    Fast Generation of Heterogeneous Mental Models from Longitudinal Data by Combining Genetic Algorithms and Fuzzy Cognitive Maps
    ( 2023-01-03) Bernard, David ; Cussat-Blanc, Sylvain ; Giabbanelli, Philippe
    Models that capture the heterogeneous perspectives of individuals are essential to test tailored interventions, such as behavior change interventions. Although Fuzzy Cognitive Maps (FCMs) have a rich history in depicting systems, they were either developed at an individual level through facilitated sessions, or created for an entire population through machine learning. The need to automatically create individual FCMs from data has started to be addressed, but the proposed solution was computationally prohibitive and thus could not be deployed over a large population. In this work, we use a state-of-the-art evolutionary algorithm (CMA-ES) to create individual FCMs by leveraging the growing availability of longitudinal data. We demonstrate on a real-world case study that our solution is both accurate and fast to compute. Our experiments on synthetic data also show that our approach can scale to a large number of measurements, but it cannot currently be applied to highly noisy datasets.