Browsing by Author "Tapia, Diego"

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    A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (BS-SCA)
    (Springer Science and Business Media Deutschland GmbH, 2022) Becerra-Rozas, Marcelo; Lemus Romani, José Isaac; Crawford, Broderick; Soto, Ricardo; Cisternas Caneo, Felipe; Trujillo Embry, Andrés; Arnao Molina, Máximo; Tapia, Diego; Castillo, Mauricio; Rubio, José Miguel
    This paper proposes a novel learnheuristic called Binary SARSA - Sine Cosine Algorithm (BS-SCA) for solving combinatorial problems. The BS-SCA is a binary version of Sine Cosine Algorithm (SCA) using SARSA to select a binarization operator. This operator is required due SCA was created to work in continuous domains. The performance of BS-SCA is benchmarked with a Q-learning version of the learnheuristic. The problem tested was the Set Covering Problem and the results show the superiority of our proposal.
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    A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems
    (2021) Lemus-Romani, Jose; Becerra-Rozas, Marcelo; Crawford, Broderick; Soto, Ricardo; Cisternas-Caneo, Felipe; Vega, Emanuel; Castillo, Mauricio; Tapia, Diego; Astorga, Gino; Palma, Wenceslao; Castro, Carlos; Garcia, Jose
    Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State-Action-Reward-State-Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.
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    Fecal cortisol levels predict breeding but not survival of females in the short-lived rodent, Octodon degus
    (2013) Ebensperger, Luis A.; Tapia, Diego; Ramirez-Estrada, Juan; Leon, Cecilia; Soto-Gamboa, Mauricio; Hayes, Loren D.
    The cort-adaptation hypothesis indicates that an association between glucocorticoid (cort) levels and fitness may vary with the extent to which reproduction or breeding effort is a major determinant of cort levels. Support for a context dependent association between cort and fitness comes mostly from relatively long-lived, bird species. We tested the hypothesis that there are gender and context (life-history) specific cort-fitness relationships in degus, a short-lived and generally semelparous social rodent. In particular, we used demographical records on a natural population to estimate adult survival through seasons and years and linked that to records of baseline cort (based on fecal cortisol metabolites). We found no evidence for a direct relationship between baseline cort and adult survival across seasons, and this lack of association was recorded irrespective of sex and life history stage. Yet, cort levels during early lactation predicted the probability that females produce a second litter during the same breeding season, supporting a connection between baseline cort levels and breeding effort. Overall, the differential effects of cort on survival and breeding supported that the extent of cort-fitness relationships depends on the fitness component examined. (C) 2013 Published by Elsevier Inc.
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    KDEL receptor regulates secretion by lysosome relocation- and autophagy-dependent modulation of lipid-droplet turnover
    (2019) Tapia, Diego; Zamora, Constanza; Espinoza, Javier; Rizzo, Riccardo; González Cárdenas, Alexis; Fuentes Peña, Danitza Natalia; Hernández, Sergio; Cavieres, Viviana A.; Guzmán, Fanny; Arriagada, Gloria; Yuseff Sepúlveda, María Isabel; Mardones, Gonzalo A.; Burgos , Patricia V.; Luini, Alberto; González, Alfonso; Cancino, Jorge; Jiménez, Tomás; Soza Gajardo, Andrea
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    Reinforcement Learning Based Whale Optimizer
    (Springer International, 2021) Becerra Rozas, Marcelo; Lemus Romani, José Isaac; Crawford, Broderick; Soto, Ricardo; Cisternas Caneo, Felipe; Embry, Andres Trujillo; Molina, Maximo Arnao; Tapia, Diego; Castillo, Mauricio; Misra, Sanjay; Rubio, Jose Miguel
    This work proposes a Reinforcement Learning based optimizer integrating SARSA and Whale Optimization Algorithm. SARSA determines the binarization operator required during the metaheuristic process. The hybrid instance is applied to solve benchmarks of the Set Covering Problem and it is compared with a Q-learning version, showing good results in terms of fitness, specifically, SARSA beats its Q-Learning version in 44 out of 45 instances evaluated. It is worth mentioning that the only instance where it does not win is a tie. Finally, thanks to graphs presented in our results analysis we can observe that not only does it obtain good results, it also obtains a correct exploration and exploitation balance as presented in the referenced literature.