Academic literature on the topic 'Genetic programming, strategies, applications'
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Journal articles on the topic "Genetic programming, strategies, applications"
Ni, He, Fan Ming Zeng, Bo Yu, and Feng Rui Sun. "The Convergence Mechanism and Strategies for Non-Elitist Genetic Programming." Applied Mechanics and Materials 347-350 (August 2013): 3850–60. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3850.
Full textZhang, Biaobiao, Yue Wu, Jiabin Lu, and K. L. Du. "Evolutionary Computation and Its Applications in Neural and Fuzzy Systems." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/938240.
Full textLi, He, and Naiyu Shi. "Application of Genetic Optimization Algorithm in Financial Portfolio Problem." Computational Intelligence and Neuroscience 2022 (July 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/5246309.
Full textSokolov, Artem, Darrell Whitley, and Andre’ da Motta Salles Barreto. "A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming." Genetic Programming and Evolvable Machines 8, no. 3 (July 31, 2007): 221–37. http://dx.doi.org/10.1007/s10710-007-9030-1.
Full textKABOUDAN, MAK. "EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS." New Mathematics and Natural Computation 01, no. 01 (March 2005): 79–107. http://dx.doi.org/10.1142/s1793005705000056.
Full textNiño, Elías, Carlos Ardila, Alfredo Perez, and Yezid Donoso. "A Genetic Algorithm for Multiobjective Hard Scheduling Optimization." International Journal of Computers Communications & Control 5, no. 5 (December 1, 2010): 825. http://dx.doi.org/10.15837/ijccc.2010.5.2243.
Full textChernov, Ivan E., and Andrey V. Kurov. "APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2022): 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.
Full textFREY, CLEMENS. "CO-EVOLUTION OF FINITE STATE MACHINES FOR OPTIMIZATION: PROMOTION OF DEVICES WHICH SEARCH GLOBALLY." International Journal of Computational Intelligence and Applications 02, no. 01 (March 2002): 1–16. http://dx.doi.org/10.1142/s1469026802000397.
Full textEdmondson, Michael C., L. Tang, and A. Kern. "Software Development Modules for Microcontroller-Based System." Advances in Science and Technology 56 (September 2008): 45–51. http://dx.doi.org/10.4028/www.scientific.net/ast.56.45.
Full textGAGNÉ, CHRISTIAN, and MARC PARIZEAU. "GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS: PRINCIPLES AND CASE-STUDY." International Journal on Artificial Intelligence Tools 15, no. 02 (April 2006): 173–94. http://dx.doi.org/10.1142/s021821300600262x.
Full textDissertations / Theses on the topic "Genetic programming, strategies, applications"
Fillon, Cyril. "New strategies for efficient and practical genetic programming." Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/2581.
Full textIn the last decades, engineers and decision makers expressed a growing interest in the development of effective modeling and simulation methods to understand or predict the behavior of many phenomena in science and engineering. Many of these phenomena are translated in mathematical models for convenience and to carry out an easy interpretation. Methods commonly employed for this purpose include, for example, Neural Networks, Simulated Annealing, Genetic Algorithms, Tabu search, and so on. These methods all seek for the optimal or near optimal values of a predefined set of parameters of a model built a priori. But in this case, a suitable model should be known beforehand. When the form of this model cannot be found, the problem can be seen from another level where the goal is to find a program or a mathematical representation which can solve the problem. According to this idea the modeling step is performed automatically thanks to a quality criterion which drives the building process. In this thesis, we focus on the Genetic Programming (GP) approach as an automatic method for creating computer programs by means of artificial evolution based upon the original contributions of Darwin and Mendel. While GP has proven to be a powerful means for coping with problems in which finding a solution and its representation is difficult, its practical applicability is still severely limited by several factors. First, the GP approach is inherently a stochastic process. It means there is no guarantee to obtain a satisfactory solution at the end of the evolutionary loop. Second, the performances on a given problem may be strongly dependent on a broad range of parameters, including the number of variables involved, the quantity of data for each variable, the size and composition of the initial population, the number of generations and so on. On the contrary, when one uses Genetic Programming to solve a problem, he has two expectancies: on the one hand, maximize the probability to obtain an acceptable solution, and on the other hand, minimize the amount of computational resources to get this solution. Initially we present innovative and challenging applications related to several fields in science (computer science and mechanical science) which participate greatly in the experience gained in the GP field. Then we propose new strategies for improving the performances of the GP approach in terms of efficiency and accuracy. We probe our approach on a large set of benchmark problems in three different domains. Furthermore we introduce a new approach based on GP dedicated to symbolic regression of multivariate data-sets where the underlying phenomenon is best characterized by a discontinuous function. These contributions aim to provide a better understanding of the key features and the underlying relationships which make enhancements successful in improving the original algorithm.
Negli ultimi anni, ingegneri e progettisti hanno espresso un interesse crescente nello sviluppo di nuovi metodi di simulazione e di modellazione per comprendere e predire il comportamento di diversi fenomeni sia in ambito scientifico che ingegneristico. Molti di questi fenomeni vengono descritti attraverso modelli matematici che ne facilitano l'interpretazione. A questo fine, i metodi più comunemente impiegati sono, le tecniche basate sui Reti Neurali, Simulated Annealing, gli Algoritmi Genetici, la ricerca Tabu, ecc. Questi metodi vanno a determinare i valori ottimali o quasi ottimali dei parametri di un modello costruito a priori. E evidente che in tal caso, si dovrebbe conoscere in anticipo un modello idoneo. Quando ciò non è possibile, il problema deve essere considerato da un altro punto di vista: l'obiettivo è trovare un programma o una rappresentazione matematica che possano risolvere il problema. A questo scopo, la fase di modellazione è svolta automaticamente in funzione di un criterio qualitativo che guida il processo di ricerca. Il tema di ricerca di questa tesi è la programmazione genetica (“Genetic Programming” che chiameremo GP) e le sue applicazioni. La programmazione genetica si può definire come un metodo automatico per la generazione di programmi attraverso una simulazione artificiale dei principi relativi all'evoluzione naturale basata sui contributi originali di Darwin e di Mendel. La programmazione genetica ha dimostrato di essere un potente mezzo per affrontare quei problemi in cui trovare una soluzione e la sua rappresentazione è difficile. Però la sua applicabilità rimane severamente limitata da diversi fattori. In primo luogo, il metodo GP è inerentemente un processo stocastico. Ciò significa che non garantisce che una soluzione soddisfacente sarà trovata alla fine del ciclo evolutivo. In secondo luogo, le prestazioni su un dato problema dipendono fortemente da una vasta gamma di parametri, compresi il numero di variabili impiegate, la quantità di dati per ogni variabile, la dimensione e la composizione della popolazione iniziale, il numero di generazioni e così via. Al contrario, un utente della programmazione genetica ha due aspettative: da una parte, massimizzare la probabilità di ottenere una soluzione accettabile, e dall'altra, minimizzare la quantità di risorse di calcolo per ottenerla. Nella fase iniziale di questo lavoro sono state considerate delle applicazioni particolarmente innovative relative a diversi campi della scienza (informatica e meccanica) che hanno contributo notevolmente all'esperienza acquisita nel campo della programmazione genetica. In questa tesi si propone un nuovo procedimento con lo scopo di migliorare le prestazioni della programmazione genetica in termini di efficienza ed accuratezza. Abbiamo testato il nostro approccio su un ampio insieme di benchmarks in tre domini applicativi diversi. Si propone inoltre una tecnica basata sul GP per la regressione simbolica di data-set multivariati dove il fenomeno di fondo è caratterizzato da una funzione discontinua. Questi contributi cercano di fornire una comprensione migliore degli elementi chiave e dei meccanismi interni che hanno consentito il miglioramento dell'algoritmo originale.
XX Ciclo
1980
Heinze, Glenn. "Application of evolutionary algorithm strategies to entity relationship diagrams /." View PDF document on the Internet, 2004. http://library.athabascau.ca/scisthesis/Heinze.pdf.
Full textDeakin, Anthony Grayham. "Evolving strategies with genetic programming." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272651.
Full textCaroli, Alberto. "Genetic Programming applications in Robotics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5860/.
Full textDe, Lorenzo Andrea. "Genetic Programming Techniques in Engineering Applications." Doctoral thesis, Università degli studi di Trieste, 2014. http://hdl.handle.net/10077/9991.
Full textMachine learning is a suite of techniques that allow developing algorithms for performing tasks by generalizing from examples. Machine learning systems, thus, may automatically synthesize programs from data. This approach is often feasible and cost-effective where manual programming or manual algorithm design is not. In the last decade techniques based on machine learning have spread in a broad range of application domains. In this thesis, we will present several novel applications of a specific machine Learning technique, called Genetic Programming, to a wide set of engineering applications grounded in real world problems. The problems treated in this work range from the automatic synthesis of regular expressions, to the generation of electricity price forecast, to the synthesis of a model for the tracheal pressure in mechanical ventilation. The results demonstrate that Genetic Programming is indeed a suitable tool for solving complex problems of practical interest. Furthermore, several results constitute a significant improvement over the existing state-of-the-art. The main contribution of this thesis is the design and implementation of a framework for the automatic inference of regular expressions from examples based on Genetic Programming. First, we will show the ability of such a framework to cope with the generation of regular expressions for solving text-extraction tasks from examples. We will experimentally assess our proposal comparing our results with previous proposals on a collection of real-world datasets. The results demonstrate a clear superiority of our approach. We have implemented the approach in a web application that has gained considerable interest and has reached peaks of more 10000 daily accesses. Then, we will apply the framework to a popular "regex golf" challenge, a competition for human players that are required to generate the shortest regular expression solving a given set of problems. Our results rank in the top 10 list of human players worldwide and outperform those generated by the only existing algorithm specialized to this purpose. Hence, we will perform an extensive experimental evaluation in order to compare our proposal to the state-of-the-art proposal in a very close and long-established research field: the generation of a Deterministic Finite Automata (DFA) from a labelled set of examples. Our results demonstrate that the existing state-of-the-art in DFA learning is not suitable for text extraction tasks. We will also show a variant of our framework designed for solving text processing tasks of the search-and-replace form. A common way to automate search-and-replace is to describe the region to be modified and the desired changes through a regular expression and a replacement expression. We will propose a solution to automatically produce both those expressions based only on examples provided by user. We will experimentally assess our proposal on real-word search-and-replace tasks. The results indicate that our proposal is indeed feasible. Finally, we will study the applicability of our framework to the generation of schema based on a sample of the eXtensible Markup Language documents. The eXtensible Markup Language documents are largely used in machine-to-machine interactions and such interactions often require that some constraints are applied to the contents of the documents. These constraints are usually specified in a separate document which is often unavailable or missing. In order to generate a missing schema, we will apply and will evaluate experimentally our framework to solve this problem. In the final part of this thesis we will describe two significant applications from different domains. We will describe a forecasting system for producing estimates of the next day electricity price. The system is based on a combination of a predictor based on Genetic Programming and a classifier based on Neural Networks. Key feature of this system is the ability of handling outliers-i.e., values rarely seen during the learning phase. We will compare our results with a challenging baseline representative of the state-of-the-art. We will show that our proposal exhibits smaller prediction error than the baseline. Finally, we will move to a biomedical problem: estimating tracheal pressure in a patient treated with high-frequency percussive ventilation. High-frequency percussive ventilation is a new and promising non-conventional mechanical ventilatory strategy. In order to avoid barotrauma and volutrauma in patience, the pressure of air insufflated must be monitored carefully. Since measuring the tracheal pressure is difficult, a model for accurately estimating the tracheal pressure is required. We will propose a synthesis of such model by means of Genetic Programming and we will compare our results with the state-of-the-art.
XXVI Ciclo
1984
Pinder, Robert William 1977. "Applications of genetic programming to parallel system optimization." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86507.
Full textIncludes bibliographical references (p. 81-84).
by Robert William Pinder.
M.Eng.
Wang, Xia. "Applications of genetic algorithms, dynamic programming, and linear programming to combinatorial optimization problems." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8778.
Full textThesis research directed by: Applied Mathematics & Statistics, and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Day, Peter. "Advances in genetic programming with applications in speech and audio." Thesis, University of Liverpool, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428373.
Full textHulse, Paul. "A study of topical applications of genetic programming and genetic algorithms in physical and engineering systems." Thesis, University of Salford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391313.
Full textStasinakis, Charalampos. "Applications of hybrid neural networks and genetic programming in financial forecasting." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4921/.
Full textBooks on the topic "Genetic programming, strategies, applications"
Kaisa, Miettinen, ed. Evolutionary algorithms in engineering and computer science: Recent advances in genetic algorithms, evolution strategies, evolutionary programming, genetic programming, and industrial applications. Chichester: Wiley, 1999.
Find full textGandomi, Amir H., Amir H. Alavi, and Conor Ryan, eds. Handbook of Genetic Programming Applications. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20883-1.
Full textMichael, Affenzeller, ed. Genetic algorithms and genetic programming: Modern concepts and practical applications. Boca Raton: Chapman & Hall/CRC, 2009.
Find full textGenetic and evolutionary computation: Medical applications. Chichester, U.K: Wiley, 2010.
Find full textL, Karr C., and Freeman L. M, eds. Industrial applications of genetic algorithms. Boca Raton, FL: CRC Press, 1999.
Find full textMaulik, Sunil. Molecular biotechnology: Therapeutic applications and strategies. New York: Wiley-Liss, 1997.
Find full textDavid, North, and Mayfield Mark, eds. Object models: Strategies, patterns, and applications. 2nd ed. Upper Saddle River, N.J: Yourdon Press, 1997.
Find full textDavid, North, and Mayfield Mark, eds. Object models: Strategies, patterns, and applications. Englewood Cliffs., N.J: Yourdon Press, 1995.
Find full textWong, Man Leung. Data mining using grammar based genetic programming and applications. Boston: Kluwer Academic, 2000.
Find full textJürgen, Borlak, ed. Handbook of toxicogenomics: Strategies and applications. Weinheim: Wiley-VCH, 2005.
Find full textBook chapters on the topic "Genetic programming, strategies, applications"
Gypteau, Jeremie, Fernando E. B. Otero, and Michael Kampouridis. "Generating Directional Change Based Trading Strategies with Genetic Programming." In Applications of Evolutionary Computation, 267–78. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16549-3_22.
Full textSwan, Jerry, Krzysztof Krawiec, and Neil Ghani. "Polytypic Genetic Programming." In Applications of Evolutionary Computation, 66–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55792-2_5.
Full textSmith, Stephen L., James Alfred Walker, and Julian F. Miller. "Medical Applications of Cartesian Genetic Programming." In Cartesian Genetic Programming, 309–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_11.
Full textDracopoulos, Dimitris C. "Genetic Algorithms and Genetic Programming for Control." In Evolutionary Algorithms in Engineering Applications, 329–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03423-1_19.
Full textJackson, David. "Evolving Defence Strategies by Genetic Programming." In Lecture Notes in Computer Science, 281–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31989-4_25.
Full textAley, Rob. "Strategies for High-Performance Applications." In Pro Functional PHP Programming, 103–45. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2958-3_5.
Full textMonsieurs, Patrick, and Eddy Flerackers. "Reducing Bloat in Genetic Programming." In Computational Intelligence. Theory and Applications, 471–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_48.
Full textLouis, Sushil J., and Gan Li. "Combining robot control strategies using genetic algorithms with memory." In Evolutionary Programming VI, 431–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0014831.
Full textSchniederjans, Marc J. "Goal Programming Model Formulation Strategies." In Goal Programming: Methodology and Applications, 21–44. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2229-4_2.
Full textJohnson, Colin G. "Genetic Programming with Guaranteed Constraints." In Applications and Science in Soft Computing, 95–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45240-9_14.
Full textConference papers on the topic "Genetic programming, strategies, applications"
Ono, Keiko, and Yoshiko Hanada. "Assembling bloat control strategies in genetic programming for image noise reduction." In 2014 14th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2014. http://dx.doi.org/10.1109/isda.2014.7066279.
Full textBokhari, Syed, and Oliver Theel. "Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008955301200129.
Full textFunie, Andreea-Ingrid, Mark Salmon, and Wayne Luk. "A Hybrid Genetic-Programming Swarm-Optimisation Approach for Examining the Nature and Stability of High Frequency Trading Strategies." In 2014 13th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2014. http://dx.doi.org/10.1109/icmla.2014.11.
Full textRaeder, Mateus, Dalvan Griebler, Lucas Baldo, and Luiz Gustavo Fernandes. "Performance Prediction of Parallel Applications with Parallel Patterns using Stochastic Methods." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2011. http://dx.doi.org/10.5753/wscad.2011.17273.
Full textBirk, Lothar, Gu¨nther F. Clauss, and June Y. Lee. "Practical Application of Global Optimization to the Design of Offshore Structures." In ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/omae2004-51225.
Full text"Session details: Evolution strategies, evolutionary programming: poster." In GECCO06: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2006. http://dx.doi.org/10.1145/3249649.
Full text"Session details: Evolutionary strategies and evolutionary programming." In GECCO05: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3249407.
Full text"Session details: Evolution strategies, evolutionary programming: papers." In GECCO06: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2006. http://dx.doi.org/10.1145/3249648.
Full text"Session details: Evolutionary strategies and evolutionary programming." In GECCO05: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3249408.
Full textAlonso, César L., José Luis Montaña, and Cruz Enrique Borges. "Evolution Strategies for Constants Optimization in Genetic Programming." In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.35.
Full textReports on the topic "Genetic programming, strategies, applications"
Spector, Lee. Multi-Type Self Adaptive Genetic Programming for Complex Applications. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada432974.
Full textLapidot, Moshe, Linda Hanley-Bowdoin, Jane E. Polston, and Moshe Reuveni. Geminivirus-resistant Tomato Plants: Combining Transgenic and Conventional Strategies for Multi-viral Resistance. United States Department of Agriculture, December 2010. http://dx.doi.org/10.32747/2010.7592639.bard.
Full textKahabuka, Catherine, Salum Mshamu, Nrupa Jani, and Kamden Hoffmann. Midterm evaluation of USAID Tulonge Afya Project. Population Council, August 2020. http://dx.doi.org/10.31899/sbsr2020.1010.
Full textRodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0003982.
Full textRon, Eliora, and Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7695860.bard.
Full textHodges, Thomas K., and David Gidoni. Regulated Expression of Yeast FLP Recombinase in Plant Cells. United States Department of Agriculture, September 2000. http://dx.doi.org/10.32747/2000.7574341.bard.
Full text