Papers

@article{Rojo-López2024,

title = {Genetic programming to understand the influence of new sustainable powder materials in the fresh performance of cement pastes},

author = {Gemma Rojo-López and Belén González-Fonteboa and Juan Luis Pérez-Ordóñez and Fernando Martínez-Abella},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189693470&doi=10.1016%2fj.jobe.2024.109186&partnerID=40&md5=82b0d972a8049ef5dccd64581bae8dd4},

doi = {10.1016/j.jobe.2024.109186},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Building Engineering},

volume = {88},

abstract = {This study focused on pastes that incorporate metakaolin, biomass ash, and granite powder as supplementary cementitious materials to obtain specific expressions to predict rheological properties in pastes and to define the most appropriate dosage parameters using genetic programming. For this purpose, a dataset was developed following a central composite design, and some fresh properties were measured: Marsh cone and rheological properties, such as yield stress and plastic viscosity. The models generated by genetic programming presented robust statistical indices for the properties studied. The influence of supplementary cementitious materials on rheological properties was also analysed through a parametric analysis. After analysing the factors affecting paste rheology, it was concluded that the most important aspects affecting fresh behaviour were water demand and particle interaction, as well as the relation between both effects. © 2024 The Author(s)},

note = {Cited by: 0; All Open Access, Green Open Access, Hybrid Gold Open Access},

keywords = {Biomass ashes, Cement paste, Cements, Fresh performance, Genetic algorithms, Genetic programming, Granite, Granite powder, Metakaolins, Parametric analysis, Performance of cement, Powder material, Rheological property, Rheology, Supplementary cementitious material, Yield stress},

pubstate = {published},

tppubtype = {article}

}

@article{Carballal20202948,

title = {A point-based redesign algorithm for designing geometrically complex surfaces. A case study: Miralles's croissant paradox},

author = {Adrián Carballal and Rafael Iván Pazos-Pérez and Nereida Rodríguez-Fernández and Iria Santos and María D. García-Vidaurrázaga and Juan R. Rabuñal-Dopico},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092543827&doi=10.1049%2fiet-ipr.2020.0223&partnerID=40&md5=52b5925bc2da8042417e2be175c511e5},

doi = {10.1049/iet-ipr.2020.0223},

issn = {17519659},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IET Image Processing},

volume = {14},

number = {12},

pages = {2948-2956},

publisher = {Institution of Engineering and Technology},

abstract = {This study explores the use of point clouds for both representation and genetic morphogenesis of complex geometry. The accurate representation of existing objects of complex curved geometry, which are subsequently geometrically modified by evolutionary morphogenetic processes, is analysed. To this end, as a method of representation and generation of complex geometries, a point-based genetic algorithm and the use of large unstructured point clouds are proposed. A study of convergence and diversity of the implemented algorithm is detailed, as well as a comparison with the Coyote optimisation algorithm in terms of representation error demonstrating its efficiency. Some commonly used three-dimensional formats in architecture, such as NURBS and polygon meshes, are analysed, and compared against point clouds. This study also includes an evaluation regarding whether the use of point clouds is a more suitable format for realistic representation, rationalisation and genetic morphogenesis. © The Institution of Engineering and Technology 2020.},

note = {cited By 3},

keywords = {Complex geometries; Complex surface; Its efficiencies; Morphogenetic process; Optimisations; Polygon meshes; Rationalisation; Unstructured point clouds, Genetic algorithms, Geometry},

pubstate = {published},

tppubtype = {article}

}

@article{paz_optimisation_2020,

title = {Optimisation of thin-walled hybrid vertical struts for crashworthy aircraft designs},

author = {Javier Paz-Méndez and Jacobo Díaz-García and Luis E. Romera-Rodríguez and F. Teixeira-Dias},

url = {https://doi.org/10.1007/s00158-019-02350-3},

doi = {10.1007/s00158-019-02350-3},

issn = {1615-1488},

year  = {2020},

date = {2020-01-01},

urldate = {2026-08-06},

journal = {Structural and Multidisciplinary Optimization},

volume = {61},

number = {1},

pages = {141–158},

abstract = {This research concerns the crashworthiness enhancement of a model of a Boeing 737-200 fuselage section. Using a validated numerical specimen, four thin-walled crushable hybrid energy absorbers are added to the aircraft to work as vertical struts. The absorbers are composed of a hollow aluminium tube, a star-shaped glass fibre–reinforced polymer inner matrix and foam extrusions. The absorbers—with variable tube edge and thickness, composite thickness and core height—are single- and multi-objectively optimised. Surrogate models and genetic algorithms are used for the minimisation of acceleration loads, injury levels and the strut’s weight. Results yield a more efficient frames’ collapse evolution with plastic dissipation increased by over 50%. Consequently, acceleration peaks are up to 50% lower at the two measured locations while maintaining low mass values. Injury levels were also reduced from severe to moderate according to an Eiband diagram.},

keywords = {Aircraft design, Biometric criteria, Crashworthiness, Genetic algorithms, Hybrid energy absorbers, Multi-objective optimisation, Surrogate models},

pubstate = {published},

tppubtype = {article}

}

@article{Pazos-Pérez2018251,

title = {Assisted surface redesign by perturbing its point cloud representation},

author = {Rafael Iván Pazos-Pérez and Adrián Carballal and Juan R. Rabuñal-Dopico and Omar A. Mures and María D. García-Vidaurrázaga},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048117856&doi=10.1049%2fiet-sen.2016.0298&partnerID=40&md5=5ab8625c238b87f769ab6440c3fed1e0},

doi = {10.1049/iet-sen.2016.0298},

issn = {17518806},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {IET Software},

volume = {12},

number = {3},

pages = {251-257},

publisher = {Institution of Engineering and Technology},

abstract = {This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured point clouds are proposed as a manipulation method of complex geometries. The intent of the algorithm is to improve the design experience, thus different solutions can be presented to designers. The main objective of this work is to provide examples to be adopted as user own or to help them in the creative process. This is not about providing them with a tool to 'do' the designer's creative work, but using it as a creative tool in which the user retains control of it. The powerfulness of this approach relies on the fact that the user can use any/diverse criteria (objective or subjective) to evaluate the individuals proposed as possible solutions. As part of this study, the convergence of the algorithm and the ability of diversity in the final populations of the search process will be demonstrated. Various examples of the use of the algorithm are displayed. © The Institution of Engineering and Technology 2018.},

note = {cited By 1},

keywords = {Complex surface; Creative process; Creative tools; Design experience; Manipulation methods; Research studies; Search process; Unstructured point clouds, Computer software, Genetic algorithms},

pubstate = {published},

tppubtype = {article}

}

@article{Aguiar-Pulido2013206,

title = {Evolutionary computation and QSAR research},

author = {Vanessa Aguiar-Pulido and Marcos Gestal and Maykel Cruz-Monteagudo and Juan R. Rabuñal-Dopico and Julián Dorado and Cristian R. Munteanu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888061749&doi=10.2174%2f1573409911309020006&partnerID=40&md5=07c2027e2d50fffe1cb23813249e5e69},

doi = {10.2174/1573409911309020006},

issn = {15734099},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {Current Computer-Aided Drug Design},

volume = {9},

number = {2},

pages = {206-225},

publisher = {Bentham Science Publishers},

abstract = {The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods. © 2013 Bentham Science Publishers.},

note = {cited By 28},

keywords = {Biological properties; Computation structure; Feature selection methods; Features extraction; High throughput screening; Molecular descriptors; Quantitative structure activity relationship; Quantitative structure-activity relationship modeling; Variables selections, Calculations; Clustering algorithms; Computational chemistry; Data mining; Feature Selection; Genetic programming; Learning systems; Libraries; Molecular graphics; Molecules, Genetic algorithms, penicillin derivative},

pubstate = {published},

tppubtype = {article}

}

@article{Pérez201282,

title = {Optimization of existing equations using a new Genetic Programming algorithm: Application to the shear strength of reinforced concrete beams},

author = {Juan Luis Pérez-Ordóñez and Antoni Cladera and Juan R. Rabuñal-Dopico and Fernando Martínez-Abella},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861865268&doi=10.1016%2fj.advengsoft.2012.02.008&partnerID=40&md5=36b8a0d0ab0f2abd048ab58c10ab79fe},

doi = {10.1016/j.advengsoft.2012.02.008},

issn = {09659978},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {Advances in Engineering Software},

volume = {50},

number = {1},

pages = {82-96},

publisher = {Elsevier Ltd},

abstract = {A method based on Genetic Programming (GP) to improve previously known empirical equations is presented. From a set of experimental data, the GP may improve the adjustment of such formulas through the symbolic regression technique. Through a set of restrictions, and the indication of the terms of the expression to be improved, GP creates new individuals. The methodology allows us to study the need of including new variables in the expression. The proposed method is applied to the shear strength of concrete beams. The results show a marked improvement using this methodology in relation to the classic GP and international code procedures. © 2012 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.},

note = {cited By 39},

keywords = {Artificial intelligence; Concrete beams and girders; Concretes; Genetic programming; Regression analysis; Shear strength; Structural design, Empirical equations; Experimental data; International codes; Programming algorithms; Reinforced concrete beams; Strength of concrete; Symbolic regression, Genetic algorithms},

pubstate = {published},

tppubtype = {article}

}

@article{Dorado2001717,

title = {Multilevel genetic algorithm for the complete development of ANN},

author = {Julián Dorado and Antonino Santos and Juan R. Rabuñal-Dopico},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902152787&doi=10.1007%2f3-540-45720-8_86&partnerID=40&md5=c2152ad9b7f8ee0a7cc2f1cfeb387d84},

doi = {10.1007/3-540-45720-8_86},

issn = {03029743},

year  = {2001},

date = {2001-01-01},

urldate = {2001-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {2084 LNCS},

number = {PART 1},

pages = {717-724},

publisher = {Springer Verlag},

abstract = {The utilization of Genetic Algorithms (GA) in the development of Artificial Neural Networks is a very active area of investigation. The works that are being carried out at present not only focus on the adjustment of the weights of the connections, but also they tend, more and more, to the development of systems which realize tasks of design and training, in parallel. To cover these necessities and, as an open platform for new developments, in this article it is shown a multilevel GA architecture which establishes a difference between the design and the training tasks. In this system, the design tasks are performed in a parallel way, by using different machines. Each design process has associated a training process as an evaluation function. Every design GA interchanges solutions in such a way that they help one each other towards the best solution working in a cooperative way during the simulation. © Springer-Verlag Berlin Heidelberg 2001.},

note = {cited By 2},

keywords = {Active area; Design process; Design tasks; Evaluation function; Multi-level genetic algorithms; Open platforms; Training process, Genetic algorithms, Neural networks; Algorithms; Artificial intelligence; Design},

pubstate = {published},

tppubtype = {article}

}