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224 results on '"Olivares, Rodrigo"'

2. 0/1 1-D Bin Packing Problem Solved by a Recent Nature-Inspired Optimizer

Author

Olivares, Rodrigo, Medina, Sebastian, Godoy, Jaime, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, and Silhavy, Petr, editor

Published
2024
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3. Reinforcement Learning Applied to PSO for Multidimensional Knapsack Problem

Author

Olivares, Rodrigo, Ríos, Víctor, Olivares, Pablo, Serrano, Benjamín, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, and Silhavy, Petr, editor

Published
2024
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4. Crow Search Algorithm Boosted by Reinforcement Learning for Feature Selection

Author

Olivares, Rodrigo, Olivares, Pablo, Ríos, Víctor, Oliveros, Alejandro, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, and Silhavy, Petr, editor

Published
2024
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5. Orca Predator Algorithm for Feature Selection

Author

Ravelo, Camilo, Medina, Sebastian, Olivares, Rodrigo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, and Silhavy, Petr, editor

Published
2024
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11. A Strategic-Operational Contribution to the One-Dimensional Multi-period Cutting Stock Problem for Sawmills

Author

Saldías, Oscar, Guerrero, Carla, Olivares, Rodrigo, Morillo-Torres, Daniel, Gatica, Gustavo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guarda, Teresa, editor, Portela, Filipe, editor, and Augusto, Maria Fernanda, editor

Published
2022
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25. An Imperialist Competitive Algorithm to Solve the Manufacturing Cell Design Problem

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Ortega, Héctor, Almonacid, Boris, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2018
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26. A Harmony Search Algorithm to Solve the Manufacturing Cell Design Problem

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Escárate, Felipe, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2018
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27. Resolving the Manufacturing Cell Design Problem via Hunting Search

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Pacheco, Nicolás, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Mouhoub, Malek, editor, Sadaoui, Samira, editor, Ait Mohamed, Otmane, editor, and Ali, Moonis, editor

Published
2018
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28. Cuckoo Search via Lévy Flight Applied to Optimal Water Supply System Design

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Castro, Carlos, Escárate, Pía, Calderón, Steve, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Mouhoub, Malek, editor, Sadaoui, Samira, editor, Ait Mohamed, Otmane, editor, and Ali, Moonis, editor

Published
2018
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29. Solving the MCDP Using a League Championship Algorithm

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Romero Fernández, Jaime, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Mouhoub, Malek, editor, Sadaoui, Samira, editor, Ait Mohamed, Otmane, editor, and Ali, Moonis, editor

Published
2018
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31. Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection.

Author

Olivares, Rodrigo, Ravelo, Camilo, Soto, Ricardo, and Crawford, Broderick

Subjects
*REINFORCEMENT learning, *DEEP reinforcement learning, *FEATURE selection, *ARTIFICIAL neural networks, *MACHINE learning, *BIOLOGICALLY inspired computing
Abstract

Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique's performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy's capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Published
2024
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32. The Impact of Using Different Choice Functions When Solving CSPs with Autonomous Search

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Niklander, Stefanie, Olguín, Eduardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fujita, Hamido, editor, Ali, Moonis, editor, Selamat, Ali, editor, Sasaki, Jun, editor, and Kurematsu, Masaki, editor

Published
2016
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33. Autonomous Search in Constraint Satisfaction via Black Hole: A Performance Evaluation Using Different Choice Functions

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Niklander, Stefanie, Olguín, Eduardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, Shi, Yuhui, editor, and Niu, Ben, editor

Published
2016
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34. Resolving the Manufacturing Cell Design Problem Using the Flower Pollination Algorithm

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, De Conti, Michele, Rubio, Ronald, Almonacid, Boris, Niklander, Stefanie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sombattheera, Chattrakul, editor, Stolzenburg, Frieder, editor, Lin, Fangzhen, editor, and Nayak, Abhaya, editor

Published
2016
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39. Online Control of Enumeration Strategies via Bat-Inspired Optimization

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Johnson, Franklin, Paredes, Fernando, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, Toledo-Moreo, Fco. Javier, editor, and Adeli, Hojjat, editor

Published
2015
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40. A Binary Cuckoo Search Algorithm for Solving the Set Covering Problem

Author

Soto, Ricardo, Crawford, Broderick, Olivares, Rodrigo, Barraza, Jorge, Johnson, Franklin, Paredes, Fernando, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, Toledo-Moreo, Fco. Javier, editor, and Adeli, Hojjat, editor

Published
2015
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41. Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization.

Author

Caselli, Nicolás, Soto, Ricardo, Crawford, Broderick, Valdivia, Sergio, Chicata, Elizabeth, and Olivares, Rodrigo

Subjects
GLOBAL optimization, METAHEURISTIC algorithms, MACHINE learning, PARTICLE swarm optimization, ALGORITHMS, SEARCH algorithms
Abstract

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Autonomous Search: Towards the Easy Tuning of Constraint Programming Solvers

Author

Crawford, Broderick, Soto, Ricardo, Olivares, Rodrigo, Herrera, Rodrigo, Monfroy, Eric, Paredes, Fernando, Junqueira Barbosa, Simone Diniz, editor, Chen, Phoebe, editor, Cuzzocrea, Alfredo, editor, Du, Xiaoyong, editor, Filipe, Joaquim, editor, Kara, Orhun, editor, Kotenko, Igor, editor, Sivalingam, Krishna M., editor, Ślęzak, Dominik, editor, Washio, Takashi, editor, Yang, Xiaokang, editor, and Stephanidis, Constantine, editor

Published
2014
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