Relevant bibliographies by topics / Meta-heuristics

Academic literature on the topic 'Meta-heuristics'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Meta-heuristics"

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Hey, Spencer Phillips. "Heuristics and Meta-heuristics in Scientific Judgement." British Journal for the Philosophy of Science 67, no. 2 (June 1, 2016): 471–95. http://dx.doi.org/10.1093/bjps/axu045.

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Yasuda, Keiichiro, and Takaaki Nagaoka. "Multipoint Search Meta-Heuristics." Proceedings of Design & Systems Conference 2003.13 (2003): 128–29. http://dx.doi.org/10.1299/jsmedsd.2003.13.128.

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Ariyaratne, M. K. A., and R. M. Silva. "Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms." International Journal of Computer Science in Sport 21, no. 1 (March 1, 2022): 49–92. http://dx.doi.org/10.2478/ijcss-2022-0003.

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Abstract This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.
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Gursoy, Arif, Mehmet Kurt, Hakan Kutucu, and Urfat Nuriyev. "New heuristics and meta-heuristics for the Bandpass problem." Engineering Science and Technology, an International Journal 20, no. 6 (December 2017): 1531–39. http://dx.doi.org/10.1016/j.jestch.2017.12.004.

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Santos, André S., Ana M. Madureira, and Leonilde R. Varela. "A Self-Parametrization Framework for Meta-Heuristics." Mathematics 10, no. 3 (February 1, 2022): 475. http://dx.doi.org/10.3390/math10030475.

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Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.
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El-Henawy, Ibrahim, and Nagham Ahmed. "Meta-Heuristics Algorithms: A Survey." International Journal of Computer Applications 179, no. 22 (February 15, 2018): 45–54. http://dx.doi.org/10.5120/ijca2018916427.

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Proll, L., I. H. Osman, and J. P. Kelly. "Meta-Heuristics Theory and Applications." Journal of the Operational Research Society 48, no. 6 (June 1997): 657. http://dx.doi.org/10.2307/3010233.

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Zaki, Shereen, and Abd El-Nasser H. Zaied. "Meta-heuristics Algorithms: A survey." International Journal of Engineering Trends and Technology 67, no. 5 (May 25, 2019): 67–74. http://dx.doi.org/10.14445/22315381/ijett-v67i5p210.

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Joshi, Susheel Kumar, and Jagdish Chand Bansal. "Parameter tuning for meta-heuristics." Knowledge-Based Systems 189 (February 2020): 105094. http://dx.doi.org/10.1016/j.knosys.2019.105094.

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Osman, I. H., and J. P. Kelly. "Meta-Heuristics Theory and Applications." Journal of the Operational Research Society 48, no. 6 (June 1997): 657. http://dx.doi.org/10.1057/palgrave.jors.2600781.

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Dissertations / Theses on the topic "Meta-heuristics"

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Mabrouk, Emad Hamdy Ahmed. "Meta-Heuristics Programming and Its Applications." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/142132.

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Koshich, P. A. "University course timetabling of meta-heuristics." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433470.

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Vázquez, Rodríguez José Antonio. "Meta-hyper-heuristics for hybrid flow shops." Thesis, University of Essex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438147.

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Gabaldon, Ponsa Eloi. "Meta-Heuristics for Scheduling in Cluster Federated Environments." Doctoral thesis, Universitat de Lleida, 2018. http://hdl.handle.net/10803/462072.

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Avui en dia, moltes organitzacions, empreses o universitats han anat acumulant, durant anys, un gran nombre de recursos computacionals agrupats en clústers. Els Entorns Clúster Federats sorgeixen com una nova arquitectura amb l’objectiu d’unir tots aquests recursos, augmentant la capacitat de còmput global de l’organització sense haver de fer una gran inversió econòmica. No obstant això, l’elevat nombre de màquines i recursos computacionals, comporten un gran consum energètic. A causa de les connotacions econòmiques i sostenibles que això implica, recentment s’ha obert una nova línia d’investigació que s’ha centrat a reduir el consum d’energia i maximitzar el rendiment de les aplicacions i la utilització dels recursos computacionals . La planificació en aquests entorns, responsable d’assignar les aplicacions als recursos del sistema, ofereix la possibilitat d’obtenir grans millores, ja que gestionar correctament els recursos pot tenir un gran impacte en el rendiment del sistema i en l’eficiència energètica. Tanmateix, aquest procés és molt complex, ja que pertany al grup de problemes NP. Aquesta tesi estudia el problema de la planificació de grans workloads extrets de diverses traces reals. Les tècniques proposades consideren l’heterogeneïtat dels recursos del sistema, així com també la capacitat d’aplicar la co-assignació per aprofitar els recursos sobrants de cada clúster. Les propostes utilitzaran tàctiques sofisticades multi-criteri, basades en Algoritmes Genètics i Particle Swarm Optimization centrades en la reducció tant del temps d’execució dels treballs com del consum energètic del sistema. Els resultats mostren l’efectivitat dels mètodes proposats, proporcionant solucions que milloren el rendiment respecte a altres tècniques presents en la literatura. Obrint una nova i interessant línia d’investigació en el camp de la planificació en entorns altament distribuïts i heterogenis.
Hoy en día, muchas organizaciones, empresas o universidades han ido acumulando, durante años, un gran número de recursos agrupados en clústeres. Los Entornos Cluster Federados surgen como una nueva arquitectura con el objetivo de unir todos estos recursos, aumentando la capacidad de cómputo global de la organización sin tener que hacer una gran inversión económica. Sin embargo, el elevado número de máquinas y recursos de computo, comportan un gran consumo energético. Debido a las connotaciones económicas y sostenibles que ello implica, recientemente se ha abierto una nueva línea de investigación que se ha centrado en reducir el consumo de energía y maximizar el rendimiento de las aplicaciones y utilización de los recursos. La planificación en estos entornos, responsable de asignar las aplicaciones a los recursos del sistema, ofrece la posibilidad de obtener grandes mejoras, ya que gestionar correctamente los recursos puede tener un gran impacto en el rendimiento del sistema y en la eficiencia energética. Sin embargo, este proceso es muy complejo, ya que pertenece al grupo de problemas NP. Esta tesis estudia el problema de la planificación de grandes workloads extraídos de distintas trazas reales. Las técnicas propuestas consideran la heterogeneidad de los recursos del sistema, así como también la capacidad de aplicar la co-asignación para aprovechar los recursos sobrantes de cada clúster. Las propuestas utilizarán tácticas sofisticadas multi-criterio, basadas en Algoritmos Genéticos y Particle Swarm Optimization centradas en la reducción tanto del tiempo de ejecución de los trabajos como del consumo energético del sistema. Los resultados muestran la efectividad de los métodos propuestos, proporcionando soluciones que mejoran el rendimiento respecto a otras técnicas presentes en la literatura. Abriendo una nueva e interesante línea de investigación en el campo de la planificación en entornos altamente distribuidos y heterogéneos.
Many organizations, companies or universities have accumulated, over the years, a large number of computing resources grouped in Clusters. Cluster Federated Environments arise as a new architecture with the objective of joining all these resources, increasing the global computing capacity of the organization without making a great economic investment. However, the high number of machines and computing resources imples great energy consumption. Due to the economic and sustainable connotations that this entails, recently a new line of investigation has focused on reducing energy consumption while maximizing the performance of the applications and the usage of the system. The scheduling in these environments, responsible for allocating the applications to the system resources, offers the possibility of obtaining great improvements, as managing the resources correctly can have a great impact on the system performance and energy efficiency. However, this process is very complex, since it belongs to the NP problem group. This PhD studies the problem of scheduling large batch workloads extracted from diverse real traces. The proposed techniques consider the heterogeneity of the system resources as well as the ability to apply co-allocation in order to take advantage of the leftover resources across clusters. The proposals will use sophisticated multi-criteria tactics, based on Genetic Algorithms and Particle Swarm Optimization, focused on reducing both the execution time of the jobs and the energy consumption of the system. The results show the effectiveness of the proposed methods, which provide solutions that improved the performance compared with other well-known techniques in the literature, opening new and interesting research lines in the scheduling field in highly distributed and heterogeneous environments.
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Wang, R. "Berth scheduling at seaports : meta-heuristics and simulation." Thesis, Liverpool John Moores University, 2018. http://researchonline.ljmu.ac.uk/9652/.

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This research aims to develop realistic solutions to enhance the efficiency of port operations. By conducting a comprehensive literature review on logistic problems at seaports, some important gaps have been identified for the first time. The following contributions are made in order to close some of the existing gaps. Firstly, this thesis identifies important realistic features which have not been well-studied in current academic research of berth planning. This thesis then aims to solve a discrete dynamic Berth allocation problem (BAP) while taking tidal constraints into account. As an important feature when dealing with realistic scheduling, changing tides have not been well-considered in BAPs. To the best of our knowledge, there is no existing work using meta-heuristics to tackle the BAP with multiple tides that can provide feasible solutions for all the test cases. We propose one single-point meta-heuristic and one population-based meta-heuristic. With our algorithms, we meet the following goals: (i) to minimise the cost of all vessels while staying in the port, and (ii) to schedule available berths for the arriving vessels taking into account a multi-tidal planning horizon. Comprehensive experiments are conducted in order to analyse the strengths and weaknesses of the algorithms and compare with both exact and approximate methods. Furthermore, lacking tools for examining existing algorithms for different optimisation problems and simulating real-world scenarios is identified as another gap in this study. This thesis develops a discrete-event simulation framework. The framework is able to generate test cases for different problems and provide visualisations. With this framework, contributions include assessing the performance of different algorithms for optimisation problems and benchmarking optimisation problems.
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Lü, Haili, and 吕海利. "A comparative study of assembly job shop scheduling using simulation, heuristics and meta-heuristics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47029018.

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Luis, Martino. "Meta-Heuristics for the Capacitated Multi-Source Weber Problem." Thesis, University of Kent, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499796.

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Mahakala, Kavya Reddy. "Identifying Security Requirements using Meta-Data and Dependency Heuristics." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543995518151544.

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Corry, Paul. "Improving efficiency in an iron foundry using meta-heuristics." Thesis, Queensland University of Technology, 2002.

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Ulker, Ozgur. "Office space allocation by using mathematical programming and meta-heuristics." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13604/.

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Office Space Allocation (OSA) is the task of efficient usage of spatial resources of an organisation. A common goal in a typical OSA problem is to minimise the wastage of space either by limiting the overuse or underuse of the facilities. The problem also contains a myriad of hard and soft constraints based on the preferences of respective organisations. In this thesis, the OSA variant usually encountered in academic institutions is investigated. Previous research in this area is rather sparse. This thesis provides a definition, extension, and literature review for the problem as well as a new parametrised data instance generator. In this thesis, two main algorithmic approaches for tackling the OSA are proposed: The first one is integer linear programming. Based on the definition of several constraints and some additional variables, two different mathematical models are proposed. These two models are not strictly alternatives to each other. While one of them provides more performance for the types of instances it is applicable, it lacks generality. The other approach provides less performance; however, it is easier to apply this model to different OSA problems. The second algorithmic approach is based on metaheuristics. A three step process in heuristic development is followed. In the first step, general local search techniques (descent methods, threshold acceptance, simulated annealing, great deluge) traverse within the neighbourhood via random relocation and swap moves. The second step of heuristic development aims to investigate large sections of the whole neighbourhood greedily via very fast cost calculation, cost update, and search for best move procedures within an evolutionary local search framework. The final step involves refinements and hybridisation of best performing (in terms of solution quality) mathematical programming and meta-heuristic techniques developed in prior steps. This thesis aims to be one of the pioneering works in the research area of OSA. The major contributions are: the analysis of the problem, a new parametrised data instance generator, mathematical programming models, and meta-heuristic approaches in order to extend the state-of-the art in this area.
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Books on the topic "Meta-heuristics"

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Osman, Ibrahim H., and James P. Kelly, eds. Meta-Heuristics. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8.

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H, Osman Ibrahim, Kelly James P. 1959-, and Meta-Heuristics International Conference (1st : 1995 : Breckenridge, Colo.), eds. Meta-heuristics: Theory & applications. Boston: Kluwer Academic, 1996.

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Osman, Ibrahim H. Meta-Heuristics: Theory and Applications. Boston, MA: Springer US, 1996.

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Xu, Jiuping, Mitsuo Gen, Zongmin Li, and YoungSu Yun. Sustainable Logistics Systems Using AI-based Meta-Heuristics Approaches. London: Routledge, 2023. http://dx.doi.org/10.4324/9781032634401.

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Vasant, Pandian. Meta-heuristics optimization algorithms in engineering, business, economics, and finance. Hershey, PA: Information Science Reference, 2013.

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Houssein, Essam Halim, Mohamed Abd Elaziz, Diego Oliva, and Laith Abualigah, eds. Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99079-4.

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Voß, Stefan, Silvano Martello, Ibrahim H. Osman, and Catherine Roucairol, eds. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5775-3.

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Vo€, Stefan. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Boston, MA: Springer US, 1999.

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Stefan, Voss, and Meta-Heuristics International Conference (2nd : 1997 : Sophia-Antipolis, France), eds. Meta-heuristics: Advances and trends in local search paradigms for optimization. Boston, Mass: Kluwer Academic Publishers, 1999.

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(Editor), Ibrahim H. Osman, and James P. Kelly (Editor), eds. Meta-Heuristics: Theory and Applications. Springer, 1996.

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Book chapters on the topic "Meta-heuristics"

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Osman, Ibrahim H., and James P. Kelly. "Meta-Heuristics: An Overview." In Meta-Heuristics, 1–21. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_1.

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Brucker, Peter, and Johann Hurink. "Complex Sequencing Problems and Local Search Heuristics." In Meta-Heuristics, 151–66. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_10.

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Dell’Amico, Mauro, Silvano Martello, and Daniele Vigo. "Heuristic Algorithms for Single Processor Scheduling with Earliness and Flow Time Penalties." In Meta-Heuristics, 167–82. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_11.

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Henze, Gregor P., Manuel Laguna, and Moncef Krarti. "Heuristics for the Optimal Control of Thermal Energy Storage." In Meta-Heuristics, 183–201. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_12.

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Mausser, Helmut E., and Stephen R. Lawrence. "Exploiting Block Structure to Improve Resource-Constrained Project Schedules." In Meta-Heuristics, 203–17. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_13.

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Lourenço, Helena Ramalhinho, and Michiel Zwijnenburg. "Combining the Large-Step Optimization with Tabu-Search: Application to The Job-Shop Scheduling Problem." In Meta-Heuristics, 219–36. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_14.

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Yamada, Takeshi, and Ryohei Nakano. "Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search." In Meta-Heuristics, 237–48. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_15.

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Fleischer, Mark A., and Sheldon H. Jacobson. "Cybernetic Optimization by Simulated Annealing: An Implementation of Parallel Processing Using Probabilistic Feedback Control." In Meta-Heuristics, 249–64. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_16.

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Lutton, J. L., and E. Philippart. "A simulated annealing algorithm for the computation of marginal costs of telecommunication links." In Meta-Heuristics, 265–75. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_17.

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Sadeh, Norman M., Yoichiro Nakakuki, and Sam R. Thangiah. "Learning to Recognize (Un)Promising Simulated Annealing Runs: Efficient Search Procedures for Job Shop Scheduling and Vehicle Routing." In Meta-Heuristics, 277–97. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_18.

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Conference papers on the topic "Meta-heuristics"

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Dass, Pranav, Harish Sharma, Jagdish Chand Bansal, and Kendall E. Nygard. "Meta heuristics for prime factorization problem." In 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE, 2013. http://dx.doi.org/10.1109/nabic.2013.6617850.

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Janssen, Frederik, and Johannes Furnkranz. "On Meta-Learning Rule Learning Heuristics." In Seventh IEEE International Conference on Data Mining (ICDM 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdm.2007.51.

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Vitor Severino, Alcemy Gabriel, and Fábio Meneghetti Ugulino de Araújo. "Meta-heuristics applied to system identification." In 24th ABCM International Congress of Mechanical Engineering. ABCM, 2017. http://dx.doi.org/10.26678/abcm.cobem2017.cob17-0542.

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M, Gokul, and Balamurali M. "Cloud Load Balancing using Meta-Heuristics." In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2022. http://dx.doi.org/10.1109/iciccs53718.2022.9788256.

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Erwin, Kyle, and Andries Engelbrecht. "Diversity Measures for Set-Based Meta-Heuristics." In 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2020. http://dx.doi.org/10.1109/iscmi51676.2020.9311572.

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Hendtlass, Tim, Irene Moser, and Marcus Randall. "Dynamic Problems and Nature Inspired Meta-Heuristics." In 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06). IEEE, 2006. http://dx.doi.org/10.1109/e-science.2006.261195.

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Sallem, A., E. Bradai, M. Kotti, E. Gaddour, M. Fakhfakh, and M. Loulou. "Optimizing CMOS current conveyors through meta heuristics." In 2009 4th International Symposium on Computational Intelligence and Intelligent Informatics (ISCIII). IEEE, 2009. http://dx.doi.org/10.1109/isciii.2009.5342264.

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Negi, Neerja, and Satish Chandra. "Web Service Composition based on Meta-heuristics." In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON). IEEE, 2022. http://dx.doi.org/10.1109/com-it-con54601.2022.9850827.

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Krömer, Pavel, Vaclav Snášel, Jan Platoš, and Ajith Abraham. "Implicit User Modelling Using Hybrid Meta-Heuristics." In 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.131.

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"Session details: Meta-heuristics and local search." In GECCO05: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3249410.

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