Articoli di riviste: "Evolution Grammaticale"

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Botne, Robert. "The Evolution of Future Tenses from Serial 'Say' Constructions in Central Eastern Bantu". Diachronica 15, n. 2 (1 gennaio 1998): 207–30. http://dx.doi.org/10.1075/dia.15.2.02bot.

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SUMMARY Future tense markers have been shown to arise from a variety of verbal sources, among them motion verbs and volitional verbs. In a small number of central eastern Bantu languages, a verb 'say' has developed into a future marker, a phenomenon not previously noted in the literature. In this study, the author presents a description and analysis of this grammatical shift, proposing two principal paths of evolution: decategorialization and auxiliation. RÉSUMÉ Les marques de temps futur proviennent d'une grande variété de sources verbales, parmi elles les verbes de mouveet et volition. Dans un petit nombre de langues bantoues du centre-est, on trouve un verbe 'dire' qui est devenu une marque de futur, un phénomène non-signalé dans la litterature jusqu'à présent. Dans l'étude actuelle l'auteur présente une description et une analyse de cette modification grammaticale, proposant deux voies principales d'évo-lu-tion: 'decategorialization' et 'auxiliation'. ZUSAMMENFASSUNG Tempusmarkierungen fur das Futur haben bekanntlich eine Reihe ver-schiedenener verbaler Ursprünge, u.a. Verben, die Bewegung oder eine Àb-sicht ausdrücken. In einer kleinen Anzahl von mittelöstlichen Bantusprachen z.B. hat sich des Verb, das gewöhnlich 'sprechen' ausdrückt, zu einem Futur-Markierungszeichen entwickelt, eine Erscheinung, die bisher nicht in der wis-senschaftlichen Literatur aufzufinden gewesen ist. In der vorliegenden Arbeit legt der Autor eine Beschreibung und Erklärung dieses grammtischen Wan-dels vor, in denen er zwei hauptsächliche Entwicklungslinien unterscheidet: 'Entkategorisierung' und 'Hilfszeitwortwerdung'.

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O'Neill, M., e C. Ryan. "Grammatical evolution". IEEE Transactions on Evolutionary Computation 5, n. 4 (2001): 349–58. http://dx.doi.org/10.1109/4235.942529.

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Nicolau, Miguel. "Understanding grammatical evolution: initialisation". Genetic Programming and Evolvable Machines 18, n. 4 (25 luglio 2017): 467–507. http://dx.doi.org/10.1007/s10710-017-9309-9.

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Bartoli, Alberto, Mauro Castelli e Eric Medvet. "Weighted Hierarchical Grammatical Evolution". IEEE Transactions on Cybernetics 50, n. 2 (febbraio 2020): 476–88. http://dx.doi.org/10.1109/tcyb.2018.2876563.

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Ortega, Alfonso, Marina de la Cruz e Manuel Alfonseca. "Christiansen Grammar Evolution: Grammatical Evolution With Semantics". IEEE Transactions on Evolutionary Computation 11, n. 1 (febbraio 2007): 77–90. http://dx.doi.org/10.1109/tevc.2006.880327.

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Dempsey, Ian, Michael O'Neill e Anthony Brabazon. "Constant creation in grammatical evolution". International Journal of Innovative Computing and Applications 1, n. 1 (2007): 23. http://dx.doi.org/10.1504/ijica.2007.013399.

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He, Pei, Colin G. Johnson e HouFeng Wang. "Modeling grammatical evolution by automaton". Science China Information Sciences 54, n. 12 (dicembre 2011): 2544–53. http://dx.doi.org/10.1007/s11432-011-4411-8.

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Hugosson, Jonatan, Erik Hemberg, Anthony Brabazon e Michael O’Neill. "Genotype representations in grammatical evolution". Applied Soft Computing 10, n. 1 (gennaio 2010): 36–43. http://dx.doi.org/10.1016/j.asoc.2009.05.003.

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Cathcart, Chundra, Gerd Carling, Filip Larsson, Niklas Johansson e Erich Round. "Areal pressure in grammatical evolution". Diachronica 35, n. 1 (16 aprile 2018): 1–34. http://dx.doi.org/10.1075/dia.16035.cat.

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Abstract This article investigates the evolutionary and spatial dynamics of typological characters in 117 Indo-European languages. We partition types of change (i.e., gain or loss) for each variant according to whether they bring about a simplification in morphosyntactic patterns that must be learned, whether they are neutral (i.e., neither simplifying nor introducing complexity) or whether they introduce a more complex pattern. We find that changes which introduce complexity show significantly less areal signal (according to a metric we devise) than changes which simplify and neutral changes, but we find no significant differences between the latter two groups. This result is compatible with a scenario where certain types of parallel change are more likely to be mediated by advergence and contact between proximate speech communities, while other developments are due purely to drift and are largely independent of intercultural contact.

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YAMAMOTO, Risako, Qingshuang YE, Hideyuki SUGIURA, Yi ZUO e Eisuke KITA. "Improvement of Grammatical Differential Evolution". Proceedings of The Computational Mechanics Conference 2016.29 (2016): 007. http://dx.doi.org/10.1299/jsmecmd.2016.29.007.

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Burke, Edmund K., Matthew R. Hyde e Graham Kendall. "Grammatical Evolution of Local Search Heuristics". IEEE Transactions on Evolutionary Computation 16, n. 3 (giugno 2012): 406–17. http://dx.doi.org/10.1109/tevc.2011.2160401.

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Kim, Hyun-Tae, e Chang Wook Ahn. "UMBGE: Univariate Model Based Grammatical Evolution". Journal of Computational and Theoretical Nanoscience 13, n. 7 (1 luglio 2016): 4104–10. http://dx.doi.org/10.1166/jctn.2016.5257.

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Yao, Jian, Qiwang Huang e Weiping Wang. "Adaptive CGFs Based on Grammatical Evolution". Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/197306.

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Computer generated forces (CGFs) play blue or red units in military simulations for personnel training and weapon systems evaluation. Traditionally, CGFs are controlled through rule-based scripts, despite the doctrine-driven behavior of CGFs being rigid and predictable. Furthermore, CGFs are often tricked by trainees or fail to adapt to new situations (e.g., changes in battle field or update in weapon systems), and, in most cases, the subject matter experts (SMEs) review and redesign a large amount of CGF scripts for new scenarios or training tasks, which is both challenging and time-consuming. In an effort to overcome these limitations and move toward more true-to-life scenarios, a study using grammatical evolution (GE) to generate adaptive CGFs for air combat simulations has been conducted. Expert knowledge is encoded with modular behavior trees (BTs) for compatibility with the operators in genetic algorithm (GA). GE maps CGFs, represented with BTs to binary strings, and uses GA to evolve CGFs with performance feedback from the simulation. Beyond-visual-range air combat experiments between adaptive CGFs and nonadaptive baseline CGFs have been conducted to observe and study this evolutionary process. The experimental results show that the GE is an efficient framework to generate CGFs in BTs formalism and evolve CGFs via GA.

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Tsoulos, Ioannis G. "Creating classification rules using grammatical evolution". International Journal of Computational Intelligence Studies 9, n. 1/2 (2020): 161. http://dx.doi.org/10.1504/ijcistudies.2020.10028134.

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Tsoulos, Ioannis G. "Creating classification rules using grammatical evolution". International Journal of Computational Intelligence Studies 9, n. 1/2 (2020): 161. http://dx.doi.org/10.1504/ijcistudies.2020.106477.

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Tsoulos, Ioannis G., Alexandros Tzallas e Evangelos Karvounis. "Applying Bounding Techniques on Grammatical Evolution". Computers 13, n. 5 (23 aprile 2024): 111. http://dx.doi.org/10.3390/computers13050111.

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The Grammatical Evolution technique has been successfully applied to some datasets from various scientific fields. However, in Grammatical Evolution, the chromosomes can be initialized at wide value intervals, which can lead to a decrease in the efficiency of the underlying technique. In this paper, a technique for discovering appropriate intervals for the initialization of chromosomes is proposed using partition rules guided by a genetic algorithm. This method has been applied to feature construction techniques used in a variety of scientific papers. After successfully finding a promising interval, the feature construction technique is applied and the chromosomes are initialized within that interval. This technique was applied to a number of known problems in the relevant literature, and the results are extremely promising.

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Tsoulos, Ioannis G., Alexandros Tzallas e Evangelos Karvounis. "Using Optimization Techniques in Grammatical Evolution". Future Internet 16, n. 5 (16 maggio 2024): 172. http://dx.doi.org/10.3390/fi16050172.

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The Grammatical Evolution technique has been successfully applied to a wide range of problems in various scientific fields. However, in many cases, techniques that make use of Grammatical Evolution become trapped in local minima of the objective problem and fail to reach the optimal solution. One simple method to tackle such situations is the usage of hybrid techniques, where local minimization algorithms are used in conjunction with the main algorithm. However, Grammatical Evolution is an integer optimization problem and, as a consequence, techniques should be formulated that are applicable to it as well. In the current work, a modified version of the Simulated Annealing algorithm is used as a local optimization procedure in Grammatical Evolution. This approach was tested on the Constructed Neural Networks and a remarkable improvement of the experimental results was shown, both in classification data and in data fitting cases.

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Lýsek, J., e J. Šťastný. "Automatic discovery of the regression model by the means of grammatical and differential evolution ". Agricultural Economics (Zemědělská ekonomika) 60, No. 12 (1 dicembre 2014): 546–52. http://dx.doi.org/10.17221/160/2014-agricecon.

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In the contribution, there is discussed the usage of the method based on the grammatical and differential evolution for the automatic discovery of regression models for discrete datasets. The combination of these two methods enables the process to find the precise structure of the mathematical model and values for the model constants separately. The used method is described and tested on the selected regression examples. The results are reported and the obtained mathematical models are presented. The advantages of the selected approach are described and compared to the classical methods.

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Tetteh, Michael, Allan de de Lima, Jack McEllin, Aidan Murphy, Douglas Mota Dias e Conor Ryan. "Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution". Algorithms 16, n. 8 (28 luglio 2023): 365. http://dx.doi.org/10.3390/a16080365.

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Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio-informatics, architecture design, financial modelling, music, software testing, game artificial intelligence and parallel programming. Multi-output problems deal with predicting numerous output variables simultaneously, a notoriously difficult problem. We present a Multi-Genome Grammatical Evolution better suited for tackling multi-output problems, specifically digital circuits. The Multi-Genome consists of multiple genomes, each evolving a solution to a single unique output variable. Each genome is mapped to create its executable object. The mapping mechanism, genetic, selection, and replacement operators have been adapted to make them well-suited for the Multi-Genome representation and the implementation of a new wrapping operator. Additionally, custom grammar syntax rules and a cyclic dependency-checking algorithm have been presented to facilitate the evolution of inter-output dependencies which may exist in multi-output problems. Multi-Genome Grammatical Evolution is tested on combinational digital circuit benchmark problems. Results show Multi-Genome Grammatical Evolution performs significantly better than standard Grammatical Evolution on these benchmark problems.

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de Lima, Allan, Samuel Carvalho, Douglas Mota Dias, Enrique Naredo, Joseph P. Sullivan e Conor Ryan. "GRAPE: Grammatical Algorithms in Python for Evolution". Signals 3, n. 3 (15 settembre 2022): 642–63. http://dx.doi.org/10.3390/signals3030039.

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GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.

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Iwasawa, Hiroto, Takuya Kuroda e Eisuke Kita. "521 Function identification by using grammatical evolution". Proceedings of The Computational Mechanics Conference 2008.21 (2008): 245–46. http://dx.doi.org/10.1299/jsmecmd.2008.21.245.

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MARUTA, Shunya, Hideyuki SUGIURA e Eisuke KITA. "An analysis of genotype in Grammatical Evolution". Proceedings of The Computational Mechanics Conference 2014.27 (2014): 251–52. http://dx.doi.org/10.1299/jsmecmd.2014.27.251.

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Cui, Wei, Anthony Brabazon e Michael O'Neill. "Dynamic trade execution: a grammatical evolution approach". International Journal of Financial Markets and Derivatives 2, n. 1/2 (2011): 4. http://dx.doi.org/10.1504/ijfmd.2011.038526.

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Gavrilis, Dimitris, Ioannis G. Tsoulos e Evangelos Dermatas. "Selecting and constructing features using grammatical evolution". Pattern Recognition Letters 29, n. 9 (luglio 2008): 1358–65. http://dx.doi.org/10.1016/j.patrec.2008.02.007.

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Byrne, Jonathan, Michael Fenton, Erik Hemberg, James McDermott e Michael O’Neill. "Optimising complex pylon structures with grammatical evolution". Information Sciences 316 (settembre 2015): 582–97. http://dx.doi.org/10.1016/j.ins.2014.03.010.

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Martín, Carlos, David Quintana e Pedro Isasi. "Grammatical Evolution-based ensembles for algorithmic trading". Applied Soft Computing 84 (novembre 2019): 105713. http://dx.doi.org/10.1016/j.asoc.2019.105713.

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Lourenço, Nuno, Francisco B. Pereira e Ernesto Costa. "Unveiling the properties of structured grammatical evolution". Genetic Programming and Evolvable Machines 17, n. 3 (3 febbraio 2016): 251–89. http://dx.doi.org/10.1007/s10710-015-9262-4.

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Medvet, Eric, Alberto Bartoli, Andrea De Lorenzo e Fabiano Tarlao. "Designing automatically a representation for grammatical evolution". Genetic Programming and Evolvable Machines 20, n. 1 (12 luglio 2018): 37–65. http://dx.doi.org/10.1007/s10710-018-9327-2.

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MIZUNO, Takao, e Eisuke KITA. "Grammatical Evolution by Using Stochastic Schemata Exploiter". Journal of Computational Science and Technology 7, n. 2 (2013): 196–204. http://dx.doi.org/10.1299/jcst.7.196.

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MARUTA, Shunya, Yuina YAZAWA e Eisuke KITA. "Effect of Population Size in Grammatical Evolution". Proceedings of The Computational Mechanics Conference 2018.31 (2018): 028. http://dx.doi.org/10.1299/jsmecmd.2018.31.028.

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Fenton, Michael, Ciaran McNally, Jonathan Byrne, Erik Hemberg, James McDermott e Michael O'Neill. "Automatic innovative truss design using grammatical evolution". Automation in Construction 39 (aprile 2014): 59–69. http://dx.doi.org/10.1016/j.autcon.2013.11.009.

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Toxir Qizi, Nazarova Shaxnoza. "DEFINITION AND LEXICAL GRAMMATICAL FEATURES OF ADVERBS". European International Journal of Multidisciplinary Research and Management Studies 02, n. 10 (1 ottobre 2022): 90–94. http://dx.doi.org/10.55640/eijmrms-02-10-16.

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Abstract (sommario):

Since the emergence of the science of linguistics, the issue of word classification has attracted the attention of experts, and discussions on this topic are still ongoing. If we observe the history of word groups, we can see that in the classification of Indians, Arabs, and Greeks, first of all, words important for speech - verbs and nouns - were separated. More precisely, the primitive classification of words, both in the East and in the West, is mainly three groups: words that indicate action (verb), words that name something (such as a thing, sign, quantity, action, state) (noun /name) and consisted of words (auxiliary words) that do not belong to these two groups. Later, as a result of scientific research conducted in this field, the boundaries and scope of word groups were determined, classification symbols of each group were developed. However, it should also be mentioned that, as in nature and society, words undergo evolution - the development of meaning. The transition of a word from one family to another sometimes takes centuries, and this, in turn, creates ongoing problems in the classification of words.

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Martín, Carlos, David Quintana e Pedro Isasi. "Dynamic Generation of Investment Recommendations Using Grammatical Evolution". International Journal of Interactive Multimedia and Artificial Intelligence 6, n. 6 (2021): 104. http://dx.doi.org/10.9781/ijimai.2021.04.007.

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Kao, Chin Ming, Li Chen, Chih Chiang Wei e You Rong Fu. "Grammatical Evolution for Total Phosphorus in Reservoir Prediction". Advanced Materials Research 211-212 (febbraio 2011): 369–73. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.369.

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The present study applied genetic programming (GP) to estimate the slump flow of high-performance concrete (HPC) using seven concrete ingredients. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover complex relationships between nonlinear systems. The results demonstrated that GP generates a more accurate formula and has lower estimating errors for predicting the slump flow of HPC than multiple linear regressions (MLRs).

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Sabar, Nasser R., Masri Ayob, Graham Kendall e Rong Qu. "Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems". IEEE Transactions on Evolutionary Computation 17, n. 6 (dicembre 2013): 840–61. http://dx.doi.org/10.1109/tevc.2013.2281527.

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SUGIURA, Hideyuki, Takao MIZUNO, Yukiko WAKITA e Eisuke KITA. "2213 Improvement of Individual Definition in Grammatical Evolution". Proceedings of The Computational Mechanics Conference 2012.25 (2012): 166–67. http://dx.doi.org/10.1299/jsmecmd.2012.25.166.

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Mizuno, Takao, Hideyuki Sugiura, Shunya Maruta, Makoto Yamauchi e Eisuke Kita. "1915 Improvement of Convergence Property of Grammatical Evolution". Proceedings of The Computational Mechanics Conference 2013.26 (2013): _1915–1_—_1915–2_. http://dx.doi.org/10.1299/jsmecmd.2013.26._1915-1_.

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Russo, Igor L. S., Heder S. Bernardino e Helio J. C. Barbosa. "A massively parallel Grammatical Evolution technique with OpenCL". Journal of Parallel and Distributed Computing 109 (novembre 2017): 333–49. http://dx.doi.org/10.1016/j.jpdc.2017.06.017.

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KURODA, Takuya, Hiroki KATO e Eisuke KITA. "1503 Application of Grammatical Evolution to Constitutive Equation". Proceedings of Design & Systems Conference 2010.20 (2010): _1503–1_—_1503–3_. http://dx.doi.org/10.1299/jsmedsd.2010.20._1503-1_.

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Adžaga, Nikola. "Automated Conjecturing of Frobenius Numbers via Grammatical Evolution". Experimental Mathematics 26, n. 2 (23 agosto 2016): 247–52. http://dx.doi.org/10.1080/10586458.2016.1175393.

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Tsoulos, Ioannis, Dimitris Gavrilis e Euripidis Glavas. "Neural network construction and training using grammatical evolution". Neurocomputing 72, n. 1-3 (dicembre 2008): 269–77. http://dx.doi.org/10.1016/j.neucom.2008.01.017.

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Martínez‐Rodríguez, David, J. Manuel Colmenar, J. Ignacio Hidalgo, Rafael‐J Villanueva Micó e Sancho Salcedo‐Sanz. "Particle swarm grammatical evolution for energy demand estimation". Energy Science & Engineering 8, n. 4 (aprile 2020): 1068–79. http://dx.doi.org/10.1002/ese3.568.

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SUKARMAN, Firdaus, e Eisuke KITA. "Automate Mobile Robot Actions Strategy Using Grammatical Evolution". Proceedings of International Conference on Design and Concurrent Engineering & Manufacturing Systems Conference 2023 (2023): 28. http://dx.doi.org/10.1299/jsmeidecon.2023.0_28.

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Rademaker, Cornelis S. M. "Gerardus Joannes Vossius (1577–1649) and the study of Latin grammar". Historiographia Linguistica 15, n. 1-2 (1 gennaio 1988): 109–28. http://dx.doi.org/10.1075/hl.15.1-2.07rad.

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Summary Gerardus Joannes Vossius (1577–1649) published his De arte grammatica libri septem in 1635. From the second edition in 1662 the work became known as Vossius’s Aristarchus. This important Latin grammar of Vossius, and also his other publications devoted to Latin, have their particular place in the evolution of grammatical studies in the 17th century. Vossius’s works were used in the first place because in them he had given a complete survey and systematization of all the scholarly information concerning Latin existing up to his own days. Neoscholastic Aristotelism was the philosophical basis of his treatment with Latin language and grammar. However, we find at the same time in Vossius’s work sometimes hints at a new approach to the study of Latin grammar. He followed in many respects the new directions pointed out by men like Scaliger and Sanctius. Thus, on the one hand, Vossius stood in the Humanist tradition of his day while, on the other, his work could be used profitably also by the Port-Royal grammarians and other philologist of the late 17th and 18th centuries. Following an appraisal of Vossius’s place in the Humanist tradition and of the contribution he made in his Aristarchus, the paper deals at some length with the analogy principle as used by Vossius and his successors. It concludes with sections on the evolution of grammatical ideas in the 17th and early 18th centuries marked especially by the tradition associated with the works of Sanctius, Vossius, and Port-Royal.

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Mingo, Jack Mario, e Ricardo Aler. "Evolution of shared grammars for describing simulated spatial scenes with grammatical evolution". Genetic Programming and Evolvable Machines 19, n. 1-2 (31 ottobre 2017): 235–70. http://dx.doi.org/10.1007/s10710-017-9315-y.

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Shulga, Mariya. "Evolution of Number Names in Modern Russian". Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije, n. 4 (dicembre 2019): 88–104. http://dx.doi.org/10.15688/jvolsu2.2019.4.7.

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The paper deals with the evolution of morphological forms and syntactic relations of numerals which give names to categories of numbers. The ways of formation of a number name тысяча / tysjatscha and its part-of-speech signs are illustrated with the texts taken from the Russian National Corpus. It's the first systematic description of transition of a noun into a number name regarded as a complex grammatical process – morphological unification of case forms (instrumental case тысячью / tysjatschju); substitution of countable noun government by coordination with it in every indirect case; loss of syntactic forms of gender and number (i.e. their ability to correlate with words in gender and number); loss of morphological forms of the plural number and their substitution by forms of the singular number; formation of compound numerals on the bases of the numeral тысяча / tysjatscha; signs of morphological separation of the number name тысяча / tysjatscha and the name of quantity. The development and grammatical adaptation of тысяча / tysjatscha in the line of numerals is reported to have taken long. The article presents a sequence of obtaining new features of the numerals. Evolution of numerals is presented as a relevant grammatical process that implements synthetic potential of the Russian language grammatical system.

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Popelka, Ondřej, e Jiří Šťastný. "WWW portal usage analysis using genetic algorithms". Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, n. 6 (2009): 201–8. http://dx.doi.org/10.11118/actaun200957060201.

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The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining). Such information is necessary in order to gain better insight into visitor’s needs and generally consumer behaviour. By leveraging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors’ behaviour in different manners depending on desired further processing. In this article we use description with a procedural programming language. Web server access log files are used as source data.The extraction of behaviour patterns can currently be solved using statistical analysis – specifically sequential analysis based methods. Our objective is to develop an alternative algorithm.The article further describes the basic algorithms of two-level grammatical evolution; this involves basic Grammatical Evolution and Differential Evolution, which forms the second phase of the computation. Grammatical evolution is used to generate the basic structure of the solution – in form of a part of application code. Differential evolution is used to find optimal parameters for this solution – the specific pages visited by a random visitor. The grammar used to conduct experiments is described along with explanations of the links to the actual implementation of the algorithm. Furthermore the fitness function is described and reasons which yield to its’ current shape. Finally the process of analyzing and filtering the raw input data is described as it is vital part in obtaining reasonable results.

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Christou, Vasileios, Ioannis Tsoulos, Alexandros Arjmand, Dimitrios Dimopoulos, Dimitrios Varvarousis, Alexandros T. Tzallas, Christos Gogos et al. "Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection". Signals 3, n. 4 (17 ottobre 2022): 737–51. http://dx.doi.org/10.3390/signals3040044.

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Hemiplegia is a condition caused by brain injury and affects a significant percentage of the population. The effect of patients suffering from this condition is a varying degree of weakness, spasticity, and motor impairment to the left or right side of the body. This paper proposes an automatic feature selection and construction method based on grammatical evolution (GE) for radial basis function (RBF) networks that can classify the hemiplegia type between patients and healthy individuals. The proposed algorithm is tested in a dataset containing entries from the accelerometer sensors of the RehaGait mobile gait analysis system, which are placed in various patients’ body parts. The collected data were split into 2-second windows and underwent a manual pre-processing and feature extraction stage. Then, the extracted data are presented as input to the proposed GE-based method to create new, more efficient features, which are then introduced as input to an RBF network. The paper’s experimental part involved testing the proposed method with four classification methods: RBF network, multi-layer perceptron (MLP) trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm, support vector machine (SVM), and a GE-based parallel tool for data classification (GenClass). The test results revealed that the proposed solution had the highest classification accuracy (90.07%) compared to the other four methods.

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Kunaver, Matevž, Árpád Bűrmen e Iztok Fajfar. "Automatic Grammatical Evolution-Based Optimization of Matrix Factorization Algorithm". Mathematics 10, n. 7 (1 aprile 2022): 1139. http://dx.doi.org/10.3390/math10071139.

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Nowadays, recommender systems are vital in lessening the information overload by filtering out unnecessary information, thus increasing comfort and quality of life. Matrix factorization (MF) is a well-known recommender system algorithm that offers good results but requires a certain level of system knowledge and some effort on part of the user before use. In this article, we proposed an improvement using grammatical evolution (GE) to automatically initialize and optimize the algorithm and some of its settings. This enables the algorithm to produce optimal results without requiring any prior or in-depth knowledge, thus making it possible for an average user to use the system without going through a lengthy initialization phase. We tested the approach on several well-known datasets. We found our results to be comparable to those of others while requiring a lot less set-up. Finally, we also found out that our approach can detect the occurrence of over-saturation in large datasets.

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KIM, Hyun-Tae, Hyun-Kyu KANG e Chang Wook AHN. "A Conditional Dependency Based Probabilistic Model Building Grammatical Evolution". IEICE Transactions on Information and Systems E99.D, n. 7 (2016): 1937–40. http://dx.doi.org/10.1587/transinf.2016edl8004.

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