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2020 – today
- 2024
[j13]Felipe-Andrés Bello Robles
, Manuel Villalobos-Cid, Max Chacón, Mario Inostroza-Ponta:
A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system. Biosyst. 241: 105231 (2024)- [c29]Jorge Cubillos-Chaparro, Márcio Dorn, Manuel Villalobos-Cid, Mario Inostroza-Ponta:
A Multiobjective Evolutionary Algorithm for Colon Cancer Biomarkers Identification on Gene Expression Data. CIBCB 2024: 1-8 - 2023
- [j12]Manuel Villalobos-Cid, Márcio Dorn, Ángela Contreras, Mario Inostroza-Ponta:
An evolutionary algorithm based on parsimony for the multiobjective phylogenetic network inference problem. Appl. Soft Comput. 139: 110270 (2023) - [c28]Nicolás Mariángel, Juan Giglio, Sebastián Aliaga-Rojas, Manuel Villalobos-Cid, Mario Inostroza-Ponta:
Evaluating the incorporation of Biological Knowledge in multiobjective clustering of gene expression data. SCCC 2023: 1-8 - 2022
- [j11]Manuel Villalobos-Cid, César Rivera, Eduardo I. Kessi-Pérez, Mario Inostroza-Ponta:
A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference. Biosyst. 213: 104606 (2022) - [c27]Vicente Rivera-Rebolledo, Manuel Villalobos-Cid, Mario Inostroza-Ponta:
Improving solution diversity on NSGA-II for multi-objective clustering problems. SCCC 2022: 1-8 - 2021
- [c26]Sebastián Aliaga-Rojas, Manuel Villalobos-Cid, Márcio Dorn, Mario Inostroza-Ponta:
A multi-objective approach for the protein structure prediction problem. SCCC 2021: 1-8 - [c25]José Fernández Goycoolea, Mario Inostroza-Ponta, Manuel Villalobos-Cid, Mauricio Marín:
Single-solution based metaheuristic approach to a novel restricted clustering problem. SCCC 2021: 1-7 - 2020
- [j10]Jorge Parraga-Alava, Mario Inostroza-Ponta:
Influence of the go-based semantic similarity measures in multi-objective gene clustering algorithm performance. J. Bioinform. Comput. Biol. 18(6): 2050038:1-2050038:21 (2020) - [j9]Manuel Villalobos-Cid, Francisco Salinas, Mario Inostroza-Ponta:
Total evidence or taxonomic congruence? A comparison of methods for combining biological evidence. J. Bioinform. Comput. Biol. 18(6): 2050040:1-2050040:17 (2020) - [j8]Mario Inostroza-Ponta, Márcio Dorn, Ivan Escobar, Leonardo de Lima Correa, Erika Rosas, Nicolas Hidalgo, Mauricio Marín:
Exploring the high selectivity of 3-D protein structures using distributed memetic algorithms. J. Comput. Sci. 41: 101087 (2020) - [c24]Leonardo de Lima Correa, Luciana Arantes, Pierre Sens, Mario Inostroza-Ponta, Márcio Dorn:
A dynamic evolutionary multi-agent system to predict the 3D structure of proteins. CEC 2020: 1-8 - [c23]César Rivera, Mario Inostroza-Ponta, Manuel Villalobos-Cid:
A multimodal multi-objective optimisation approach to deal with the phylogenetic inference problem. CIBCB 2020: 1-7 - [c22]Angelo Carlier González, José Lillo, Mario Inostroza-Ponta, Manuel Villalobos-Cid:
Evaluating the categorisation of the public hospitals in Chile according to case-mix complexity: a genetic algorithm approach. SCCC 2020: 1-9
2010 – 2019
- 2019
- [j7]Manuel Villalobos-Cid, Márcio Dorn, Rodrigo Ligabue-Braun, Mario Inostroza-Ponta:
A Memetic Algorithm Based on an NSGA-II Scheme for Phylogenetic Tree Inference. IEEE Trans. Evol. Comput. 23(5): 776-787 (2019) - [c21]Juan Giglio, Mario Inostroza-Ponta, Manuel Villalobos-Cid:
A multi-objective optimisation evolutionary approach for the Multidimensional Scaling Problem. SCCC 2019: 1-8 - [c20]Jorge Parraga-Alava, Roberto Acuña Caicedo, José Manuel Gómez, Mario Inostroza-Ponta:
An Unsupervised Learning Approach for Automatically to Categorize Potential Suicide Messages in Social Media. SCCC 2019: 1-8 - [c19]Manuel Villalobos-Cid, Maximiliano Orellana, Oscar C. Vásquez, Eduardo Pinto-Sothers, Mario Inostroza-Ponta:
Dealing with the Balanced Academic Curriculum Problem considering the Chilean Academic Credit Transfer System. SCCC 2019: 1-7 - [p1]Claudio Sanhueza Lobos, Natalie Jane de Vries, Mario Inostroza-Ponta, Regina Berretta, Pablo Moscato:
Visualizing Products and Consumers: A Gestalt Theory Inspired Method. Business and Consumer Analytics: New Ideas 2019: 661-689 - 2018
- [j6]Jorge Parraga-Alava, Márcio Dorn, Mario Inostroza-Ponta:
A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies. BioData Min. 11(1): 16:1-16:22 (2018) - [j5]Leonardo de Lima Correa, Bruno Borguesan, Camilo Farfán, Mario Inostroza-Ponta, Márcio Dorn:
A Memetic Algorithm for 3D Protein Structure Prediction Problem. IEEE ACM Trans. Comput. Biol. Bioinform. 15(3): 690-704 (2018) - [c18]Bruno Borguesan, Pedro Henrique Narloch, Mario Inostroza-Ponta, Márcio Dorn:
A Genetic Algorithm Based on Restricted Tournament Selection for the 3D-PSP Problem. CEC 2018: 1-8 - [c17]Manuel Villalobos-Cid, Márcio Dorn, Mario Inostroza-Ponta:
Performance Comparison of Multi-Objective Local Search Strategies to Infer Phylogenetic Trees. CEC 2018: 1-8 - [c16]Manuel Villalobos-Cid, Márcio Dorn, Mario Inostroza-Ponta:
Understanding the Relationship Between Decision and Objective Space in the Multi-Objective Phylogenetic Inference Problem. CEC 2018: 1-8 - [c15]Jorge Parraga-Alava, Gustavo Molina Garzon, Roberth Alcivar Cevallos, Mario Inostroza-Ponta:
Unsupervised Pattern Recognition for Geographical Clustering of Seismic Events Post MW 7.8 Ecuador Earthquake. SCCC 2018: 1-8 - [r1]Mario Inostroza-Ponta, Natalie Jane de Vries, Pablo Moscato:
World's Best Universities and Personalized Rankings. Handbook of Heuristics 2018: 1335-1371 - 2017
- [j4]Jorge Parraga-Alava, Mario Inostroza-Ponta:
A Bi-Objective Clustering Algorithm for Gene Expression Data. CLEI Electron. J. 20(2) (2017) - [j3]Bruno Borguesan, Mario Inostroza-Ponta, Márcio Dorn:
NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins. J. Comput. Biol. 24(3): 255-265 (2017) - [c14]Leonardo de Lima Correa, Mario Inostroza-Ponta, Márcio Dorn:
An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures. CEC 2017: 1111-1118 - [c13]Jorge Parraga-Alava, Márcio Dorn, Mario Inostroza-Ponta:
Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem. CEC 2017: 1119-1126 - [c12]Benjamin Ruiz-Tagle, Manuel Villalobos-Cid, Márcio Dorn, Mario Inostroza-Ponta:
Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem. SCCC 2017: 1-12 - [c11]Roberto Sandoval-Soto, Manuel Villalobos-Cid, Mario Inostroza-Ponta:
Tackling the bi-objective quadratic assignment problem by characterizing different memory strategies in a memetic algorithm. SCCC 2017: 1-12 - [c10]Manuel Villalobos-Cid, Daniel Vega-Araya, Mario Inostroza-Ponta:
Application of different multi-objective decision making techniques in the phylogenetic inference problem. SCCC 2017: 1-9 - 2016
- [j2]Manuel Villalobos-Cid, Max Chacón, Pedro Zitko, Mario Inostroza-Ponta:
A New Strategy to Evaluate Technical Efficiency in Hospitals Using Homogeneous Groups of Casemix - How to Evaluate When There is Not DRGs? J. Medical Syst. 40(4): 103:1-103:12 (2016) - [c9]Jorge Parraga-Alava, Mario Inostroza-Ponta:
A bi-objective model for gene clustering combining expression data and external biological knowledge. CLEI 2016: 1-12 - [c8]Ivan Escobar, Nicolas Hidalgo, Mario Inostroza-Ponta, Mauricio Marín, Erika Rosas, Márcio Dorn:
Evaluation of a combined energy fitness function for a distributed memetic algorithm to tackle the 3D protein structure prediction problem. SCCC 2016: 1-10 - 2015
- [j1]Bruno Borguesan, Mariel Barbachan e Silva, Bruno Grisci, Mario Inostroza-Ponta, Márcio Dorn:
APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction. Comput. Biol. Chem. 59: 142-157 (2015) - [c7]Matthew Harris, Regina Berretta, Mario Inostroza-Ponta, Pablo Moscato:
A Memetic Algorithm for the Quadratic Assignment Problem with parallel local search. CEC 2015: 838-845 - [c6]Mario Inostroza-Ponta, Camilo Farfán, Márcio Dorn:
A Memetic Algorithm for Protein Structure Prediction based on Conformational Preferences of Aminoacid Residues. GECCO (Companion) 2015: 1403-1404 - 2013
- [c5]Márcio Dorn, Mario Inostroza-Ponta, Luciana S. Buriol, Hugo Verli:
A knowledge-based genetic algorithm to predict three-dimensional structures of polypeptides. IEEE Congress on Evolutionary Computation 2013: 1233-1240 - 2011
- [c4]Ahmed Shamsul Arefin, Mario Inostroza-Ponta, Luke Mathieson, Regina Berretta, Pablo Moscato:
Clustering Nodes in Large-Scale Biological Networks Using External Memory Algorithms. ICA3PP (2) 2011: 375-386 - [c3]Hugo Meneses, Mario Inostroza-Ponta:
Evaluating Memory Schemas in a Memetic Algorithm for the Quadratic Assignment Problem. SCCC 2011: 14-18
2000 – 2009
- 2007
- [c2]Mario Inostroza-Ponta, Alexandre Mendes, Regina Berretta, Pablo Moscato:
An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity. ACAL 2007: 156-167 - 2006
- [c1]Mario Inostroza-Ponta, Regina Berretta, Alexandre Mendes, Pablo Moscato:
An automatic graph layout procedure to visualize correlated data. IFIP AI 2006: 179-188
Coauthor Index
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last updated on 2024-10-23 20:35 CEST by the dblp team
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