Relevant bibliographies by topics / Grammatical Evolution

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Grammatical Evolution'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Grammatical Evolution"

1

O'Neill, M., and C. Ryan. "Grammatical evolution." IEEE Transactions on Evolutionary Computation 5, no. 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, no. 4 (2017): 467–507. http://dx.doi.org/10.1007/s10710-017-9309-9.

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Bartoli, Alberto, Mauro Castelli, and Eric Medvet. "Weighted Hierarchical Grammatical Evolution." IEEE Transactions on Cybernetics 50, no. 2 (2020): 476–88. http://dx.doi.org/10.1109/tcyb.2018.2876563.

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Ortega, Alfonso, Marina de la Cruz, and Manuel Alfonseca. "Christiansen Grammar Evolution: Grammatical Evolution With Semantics." IEEE Transactions on Evolutionary Computation 11, no. 1 (2007): 77–90. http://dx.doi.org/10.1109/tevc.2006.880327.

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Dempsey, Ian, Michael O'Neill, and Anthony Brabazon. "Constant creation in grammatical evolution." International Journal of Innovative Computing and Applications 1, no. 1 (2007): 23. http://dx.doi.org/10.1504/ijica.2007.013399.

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He, Pei, Colin G. Johnson, and HouFeng Wang. "Modeling grammatical evolution by automaton." Science China Information Sciences 54, no. 12 (2011): 2544–53. http://dx.doi.org/10.1007/s11432-011-4411-8.

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Hugosson, Jonatan, Erik Hemberg, Anthony Brabazon, and Michael O’Neill. "Genotype representations in grammatical evolution." Applied Soft Computing 10, no. 1 (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, and Erich Round. "Areal pressure in grammatical evolution." Diachronica 35, no. 1 (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,
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YAMAMOTO, Risako, Qingshuang YE, Hideyuki SUGIURA, Yi ZUO, and 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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Tsoulos, Ioannis G., Alexandros Tzallas, and Evangelos Karvounis. "Using Optimization Techniques in Grammatical Evolution." Future Internet 16, no. 5 (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 t
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Dissertations / Theses on the topic "Grammatical Evolution"

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Harper, Robin Thomas Ross Computer Science &amp Engineering Faculty of Engineering UNSW. "Enhancing grammatical evolution." Awarded by:University of New South Wales. Computer Science & Engineering, 2010. http://handle.unsw.edu.au/1959.4/44843.

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Grammatical Evolution (GE) is a method of utilising a general purpose evolutionary algorithm to ???evolve??? programs written in an arbitrary BNF grammar. This thesis extends GE as follows: GE as an extension of Genetic Programming (GP) A novel method of automatically extracting information from the grammar is introduced. This additional information allows the use of GP style crossover which in turn allows GE to perform identically to a strongly typed GP system as well as a non-typed (or canonical) GP system. Two test problems are presented one which is more easily solved by the GP style cros
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Georgiou, Loukas. "Constituent grammatical evolution." Thesis, Bangor University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569460.

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Evolutionary algorithms are a competent nature-inspired approach for complex computational problem solving. One recent development is Grammatical Evolution, a grammar-based evolutionary algorithm which uses genotypes of variable length binary strings and a unique genotype-to-phenotype mapping process based on a BNF grammar definition describing the output language that is able to create valid individuals of an arbitrary structure or programming language. This study surveys Grammatical Evolution, identifies its most important issues, investigates the competence of the algorithm in a series of a
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Zhang, Andrew H. M. Eng Massachusetts Institute of Technology. "Structured Grammatical Evolution applied to program synthesis." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122995.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 27).<br>Grammatical Evolution (GE) is an evolutionary algorithm that is gaining popularity due to its ability to solve problems where it would be impossible to explore every solution within a realistic time. Structured Grammati
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Deodhar, Sushamna Shriniwas. "Using Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions in Genetic Epidemiology." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-10302009-181439/.

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A major goal of human genetics is the discovery and validation of genetic polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic models. This etiological complexity, coupled with rapid advances in genotyping technology present enormous theoretical and practical concerns for statistical and computational analysis. Specifically, the challenge presented by epistasis, or gene-gene interactions, has sparked the development of a multitude of statistical techniques over the year
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Neupane, Aadesh. "Emergence of Collective Behaviors in Hub-Based Colonies using Grammatical Evolution and Behavior Trees." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8827.

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Animals such as bees, ants, birds, fish, and others are able to efficiently perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators without centralized control or coordination. These complex collective behaviors are the result of emergence. Conventionally, mimicking these collective behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as algorithms. Since the conventional approach is very time consuming and cumbersome, this thesis uses an emergence-based method for the efficient evo
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Eilert, Pernilla. "Learning behaviour trees for simulated fighter pilots in airborne reconnaissance missions : A grammatical evolution approach." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156165.

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Fighter pilots often find themselves in situations where they need to make quick decisions. Therefore an intelligent decision support system that suggests how the fighter pilot should act in a specific situation is vital. The aim of this project is to investigate and evaluate grammatical evolution paired with behaviour trees to develop a decision support system. This support system should control a simulated fighter pilot during an airborne reconnaissance mission. This thesis evaluates the complexity of the evolved trees and the performance, and robustness of the algorithm. Key factors were id
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De, Silva Anthony Mihirana. "Grammar based feature generation for time-series prediction." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10278.

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The application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This thesis proposes a systematic way for generating suitable features using context-free grammar. The notion of grammar families as a compact representation to generate a broad class of features is exploited. Implementation issues and wa
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Mehrmand, Arash. "A Factorial Experiment on Scalability of Search-based Software Testing." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4224.

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Software testing is an expensive process, which is vital in the industry. Construction of the test-data in software testing requires the major cost and knowing which method to use in order to generate the test data is very important. This paper discusses the performance of search-based algorithms (preferably genetic algorithm) versus random testing, in software test-data generation. A factorial experiment is designed so that, we have more than one factor for each experiment we make. Although many researches have been done in the area of automated software testing, this research differs from al
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Noorian, Farzad. "Risk Management using Model Predictive Control." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14282.

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Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing o
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Miškařík, Kamil. "Gramatická evoluce - Java/Matlab implementace." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-230931.

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Books on the topic "Grammatical Evolution"

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O’Neill, Michael, and Conor Ryan. Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4.

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Ryan, Conor, Michael O'Neill, and JJ Collins, eds. Handbook of Grammatical Evolution. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78717-6.

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Dempsey, Ian, Michael O’Neill, and Anthony Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00314-1.

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O'Neill, Michael. Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Kluwer Academic Publishers, 2004.

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O'Neill, Michael. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Springer US, 2003.

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1972-, Ryan Conor, ed. Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Kluwer Academic Publishers, 2003.

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O'Neill, Michael, Conor Ryan, and JJ Collins. Handbook of Grammatical Evolution. Springer, 2018.

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Handbook of Grammatical Evolution. Springer, 2019.

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O'Neill, Michael, Ian Dempsey, and Anthony Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Springer London, Limited, 2009.

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O'Neill, Michael, Ian Dempsey, and Anthony Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Springer, 2010.

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Book chapters on the topic "Grammatical Evolution"

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O’Neil, Michael, and Conor Ryan. "Grammatical Evolution." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_4.

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De Silva, Anthony Mihirana, and Philip H. W. Leong. "Grammatical Evolution." In SpringerBriefs in Applied Sciences and Technology. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-411-5_3.

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Grammatical Evolution." In Natural Computing Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_19.

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Dempsey, Ian, Michael O’Neill, and Anthony Brabazon. "Grammatical Evolution." In Foundations in Grammatical Evolution for Dynamic Environments. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00314-1_2.

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O’Neil, Michael, and Conor Ryan. "Introduction." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_1.

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O’Neil, Michael, and Conor Ryan. "Survey Of Evolutionary Automatic Programming." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_2.

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O’Neil, Michael, and Conor Ryan. "Lessons From Molecular Biology." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_3.

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O’Neil, Michael, and Conor Ryan. "Four Examples of Grammatical Evolution." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_5.

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O’Neil, Michael, and Conor Ryan. "Analysis of Grammatical Evolution." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_6.

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O’Neil, Michael, and Conor Ryan. "Crossover in Grammatical Evolution." In Grammatical Evolution. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_7.

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Conference papers on the topic "Grammatical Evolution"

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Ryan, Conor. "Grammatical evolution." In the 11th annual conference companion. ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570408.

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Ryan, Conor M. "Grammatical evolution." In the 2007 GECCO conference companion. ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274126.

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Timperley, Christopher, and Susan Stepney. "Reflective Grammatical Evolution." In Artificial Life 14: International Conference on the Synthesis and Simulation of Living Systems. The MIT Press, 2014. http://dx.doi.org/10.7551/978-0-262-32621-6-ch013.

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Medvet, Eric. "Hierarchical grammatical evolution." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3075972.

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Timperley, Christopher, and Susan Stepney. "Reflective Grammatical Evolution." In Artificial Life 14: International Conference on the Synthesis and Simulation of Living Systems. The MIT Press, 2014. http://dx.doi.org/10.1162/978-0-262-32621-6-ch013.

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Ryan, Conor. "Grammatical evolution tutorial." In the 12th annual conference comp. ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830900.

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Dempsey, Ian, Michael O'Neill, and Anthony Brabazon. "Meta-grammar constant creation with grammatical evolution by grammatical evolution." In the 2005 conference. ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068289.

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Murphy, Eoin, Michael O'Neill, Edgar Galvan-Lopez, and Anthony Brabazon. "Tree-adjunct grammatical evolution." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586497.

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OGURA, MIEKO, and WILLIAM S.-Y. WANG. "EVOLUTION OF GRAMMATICAL FORMS." In Proceedings of the 8th International Conference (EVOLANG8). WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814295222_0032.

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Medvet, Eric, Fabio Daolio, and Danny Tagliapietra. "Evolvability in grammatical evolution." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3071178.3071298.

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