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Relevant bibliographies by topics / Evolutionary computation applications

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Academic literature on the topic 'Evolutionary computation applications'

Author: Grafiati

Published: 16 July 2024

Last updated: 31 July 2025

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Journal articles on the topic "Evolutionary computation applications"

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Hong, Tzung-Pei, Chuan-Kang Ting, and Oliver Kramer. "Theory and Applications of Evolutionary Computation." Applied Computational Intelligence and Soft Computing 2010 (2010): 1–2. http://dx.doi.org/10.1155/2010/360796.

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Easson, Greg, and H. G. Momm. "Evolutionary Computation for Remote Sensing Applications." Geography Compass 4, no. 3 (2010): 172–92. http://dx.doi.org/10.1111/j.1749-8198.2009.00309.x.

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Pinho, Jorge, João Luis Sobral, and Miguel Rocha. "Parallel evolutionary computation in bioinformatics applications." Computer Methods and Programs in Biomedicine 110, no. 2 (2013): 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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Cicirello, Vincent A. "Evolutionary Computation: Theories, Techniques, and Applications." Applied Sciences 14, no. 6 (2024): 2542. http://dx.doi.org/10.3390/app14062542.

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Kozlov, AP. "Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution." Evolutionary Bioinformatics 18 (January 2022): 117693432211106. http://dx.doi.org/10.1177/11769343221110654.

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The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stor

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de Nobel, Jacob. "Conference Overview: Evolutionary Computation Theory and Applications (ECTA) 2024." ACM SIGEVOlution 18, no. 1 (2025): 1–3. https://doi.org/10.1145/3733097.3733099.

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Last year's Evolutionary Computation Theory and Applications (ECTA 2024) conference took place in the beautiful city of Porto, Portugal, from November 20th to 22nd, 2024, as part of the International Joint Conference on Computational Intelligence (IJCCI). The event, organized by INSTICC and supported by ACM SIGAI, AAAI, and other esteemed institutions, brought together researchers worldwide to discuss their latest advancements.

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Yar, Morteza Husainy, Vahid Rahmati, and Hamid Reza Dalili Oskouei. "A Survey on Evolutionary Computation: Methods and Their Applications in Engineering." Modern Applied Science 10, no. 11 (2016): 131. http://dx.doi.org/10.5539/mas.v10n11p131.

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Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It’s based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally catego

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Zhang, 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.

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the univers

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Doerr, Benjamin, and Thomas Jansen. "Theory of Evolutionary Computation." Algorithmica 59, no. 3 (2010): 299–300. http://dx.doi.org/10.1007/s00453-010-9472-3.

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Holmes, John H. "Methods and applications of evolutionary computation in biomedicine." Journal of Biomedical Informatics 49 (June 2014): 11–15. http://dx.doi.org/10.1016/j.jbi.2014.05.008.

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Dissertations / Theses on the topic "Evolutionary computation applications"

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Pridgeon, Carey. "Diverse applications of evolutionary computation in bioinformatics : hypermotifs and gene regulatory network inference." Thesis, University of Exeter, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479210.

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Abraham, Ajith 1968. "Hybrid soft computing : architecture optimization and applications." Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.

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Rahman, Izaz Ur. "Novel particle swarm optimization algorithms with applications in power systems." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12219.

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Optimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods

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Kok, Jonathan. "Design methodologies and architectures of hardware-based evolutionary algorithms for aerospace optimisation applications on FPGAS." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/72904/5/Jonathan_Kok_Thesis.pdf.

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This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospac

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Ranjeet, Tirtha. "Coevolutionary algorithms for the optimization of strategies for red teaming applications." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/558.

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Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as inte

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Creaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.

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Sharifi, Soroosh. "Application of evolutionary computation to open channel flow modelling." Thesis, University of Birmingham, 2009. http://etheses.bham.ac.uk//id/eprint/478/.

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This thesis examines the application of two evolutionary computation techniques to two different aspects of open channel flow. The first part of the work is concerned with evaluating the ability of an evolutionary algorithm to provide insight and guidance into the correct magnitude and trend of the three parameters required in order to successfully apply a quasi 2D depth averaged Reynolds Averaged Navier Stokes (RANS) model to the flow in prismatic open channels. The RANS modeled adopted is the Shiono Knight Method (SKM) which requires three input parameters in order to provide closure, i.e. t

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Hayward, Kevin. "Application of evolutionary algorithms to engineering design." University of Western Australia. School of Mechanical Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0018.

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The efficiency of the mechanical design process can be improved by the use of evolutionary algorithms. Evolutionary algorithms provide a convenient and robust method to search for appropriate design solutions. Difficult non-linear problems are often encountered during the mechanical engineering design process. Solutions to these problems often involve computationally-intensive simulations. Evolutionary algorithms tuned to work with a small number of solution iterations can be used to automate the search for optimal solutions to these problems. An evolutionary algorithm was designed to give rel

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Wiltshire, Serge William. "On The Application Of Computational Modeling To Complex Food Systems Issues." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1077.

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Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of live

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Antão, Tiago Rodrigues. "Evolutionary applications of population genetics with a focus on malaria : a computational approach." Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569901.

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Malaria is a major public health concern for the one-third of the human population esti- mated to be exposed to the threat of the most virulent species, Plasmodium falciparum. Modern molecular and computational tools from population genetics may help to better understand and fight the burden of drug resistant malaria. Malaria biology is substantially different from the underlying paradigm of standard population genetics models, most notably because malaria has both a asexual haploid phase and a sexual diploid phase where selfing (i.e. mating between genetically identical parasites) is possible

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Books on the topic "Evolutionary computation applications"

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Castillo, Pedro A., and Juan Luis Jiménez Laredo, eds. Applications of Evolutionary Computation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72699-7.

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Jiménez Laredo, Juan Luis, J. Ignacio Hidalgo, and Kehinde Oluwatoyin Babaagba, eds. Applications of Evolutionary Computation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02462-7.

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Sim, Kevin, and Paul Kaufmann, eds. Applications of Evolutionary Computation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8.

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Mora, Antonio M., and Giovanni Squillero, eds. Applications of Evolutionary Computation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16549-3.

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Squillero, Giovanni, and Kevin Sim, eds. Applications of Evolutionary Computation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55792-2.

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Squillero, Giovanni, and Kevin Sim, eds. Applications of Evolutionary Computation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3.

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Di Chio, Cecilia, Alexandros Agapitos, Stefano Cagnoni, et al., eds. Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29178-4.

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Di Chio, Cecilia, Anthony Brabazon, Gianni A. Di Caro, et al., eds. Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20520-0.

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Di Chio, Cecilia, Stefano Cagnoni, Carlos Cotta, et al., eds. Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20525-5.

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Esparcia-Alcázar, Anna I., and Antonio M. Mora, eds. Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45523-4.

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Book chapters on the topic "Evolutionary computation applications"

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Mitra, Tamoghna, Henrik SaxÉN, and Nirupam Chakraborti. "Chapter 4 Evolutionary Algorithms In Ironmaking Applications." In Evolutionary Computation. Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-5.

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Singru, Pravin M., Vishal Jain, Nikilesh Krishnakumar, A. Garg, K. Tai, and V. Vijayaraghavan. "Chapter 7 Gene Expression Programming In Nanotechnology Applications." In Evolutionary Computation. Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-8.

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Coello Coello, Carlos A., David A. Van Veldhuizen, and Gary B. Lamont. "Applications." In Genetic Algorithms and Evolutionary Computation. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5184-0_6.

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Trivedi, Vibhu, and Manojkumar Ramteke. "Chapter 2 Bio-Mimetic Adaptations Of Genetic Algorithm And Their Applications To Chemical Engineering." In Evolutionary Computation. Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-3.

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Zhang, Gexiang, Mario J. Pérez-Jiménez, and Marian Gheorghe. "Fundamentals of Evolutionary Computation." In Real-life Applications with Membrane Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55989-6_2.

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Howard, D. "Bioinformatics with Evolutionary Computation." In Real World Applications of Computational Intelligence. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11364160_8.

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Whigham, P. A., and G. B. Fogel. "Ecological Applications of Evolutionary Computation." In Ecological Informatics. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05150-4_4.

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Gulisano, Vincenzo, and Eric Medvet. "Evolutionary Computation Meets Stream Processing." In Applications of Evolutionary Computation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56852-7_24.

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Kawulok, Michal, Pawel Benecki, Daniel Kostrzewa, and Lukasz Skonieczny. "Towards Evolutionary Super-Resolution." In Applications of Evolutionary Computation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8_33.

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Yaman, Anil, Ahmed Hallawa, Matt Coler, and Giovanni Iacca. "Presenting the ECO: Evolutionary Computation Ontology." In Applications of Evolutionary Computation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3_39.

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Conference papers on the topic "Evolutionary computation applications"

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Cicirello, Vincent. "Open Source Evolutionary Computation with Chips-n-Salsa." In 16th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013040600003837.

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Felipe Coello Castillo, Carlos, and Carlos A. Coello. "A Survey of Applications of Multi-Objective Evolutionary Algorithms in Biotechnology." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612023.

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Angeletti, Federica, Federico Succetti, Massimo Panella, and Antonello Rosato. "A Neural Network Symbolic Approach to Structural Health Monitoring in Aerospace Applications." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612130.

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Pérez-Espinosa, Guillermo, Jorge M. Cruz-Duarte, Ivan Amaya, José C. Ortiz-Bayliss, Hugo Terashima-Marin, and Nelishia Pillay. "Tailoring Metaheuristics for Designing Thermodynamic-Optimal Cooling Devices for Microelectronic Thermal Management Applications." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611938.

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Smith, Stephen L. "Medical Applications of Evolutionary Computation." In GECCO '15: Genetic and Evolutionary Computation Conference. ACM, 2015. http://dx.doi.org/10.1145/2739482.2756567.

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Smith, Stephen L. "Medical applications of evolutionary computation." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605364.

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Smith, Stephen L. "Medical applications of evolutionary computation." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3207873.

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Smith, Stephen L. "Medical Applications of Evolutionary Computation." In GECCO '16: Genetic and Evolutionary Computation Conference. ACM, 2016. http://dx.doi.org/10.1145/2908961.2926997.

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Julstrom, Bryant A. "Session details: Applications of evolutionary computation." In SAC '08: The 2008 ACM Symposium on Applied Computing. ACM, 2008. http://dx.doi.org/10.1145/3260572.

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Torresen, Jim. "Addressing ethical challenges within evolutionary computation applications." In GECCO '20: Genetic and Evolutionary Computation Conference. ACM, 2020. http://dx.doi.org/10.1145/3377929.3389894.

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