Relevant bibliographies by topics / Population-based algorithm

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

Academic literature on the topic 'Population-based algorithm'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Population-based algorithm"

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Tang, Ke, Fei Peng, Guoliang Chen, and Xin Yao. "Population-based Algorithm Portfolios with automated constituent algorithms selection." Information Sciences 279 (September 2014): 94–104. http://dx.doi.org/10.1016/j.ins.2014.03.105.

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M., M. H. Elroby, F. Mekhamer S., E. A. Talaat H., and A. Moustafa. Hassan M. "Population based optimization algorithms improvement using the predictive particles." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3261–74. https://doi.org/10.11591/ijece.v10i3.pp3261-3274.

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A new efficient improvement, called Predictive Particle Modification (PPM),is proposed in this paper. This modification makes the particle look tothe near area before moving toward the best solution of the group.This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance ofPPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using23 standard benchmark functions. The effectiveness of these modificationsare compared with the other un
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Folly, Komla A. "An Improved Population-Based Incremental Learning Algorithm." International Journal of Swarm Intelligence Research 4, no. 1 (2013): 35–61. http://dx.doi.org/10.4018/jsir.2013010102.

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Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of optimization problems in engineering with promising results. PBIL combines aspects of Genetic Algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, a PBIL with adapting learning rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space at the start of the run and maintain the di
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Poudel, Yam, Jeewan Phuyal, and Rajiv Kumar. "Comprehensive Study of Population Based Algorithms." American Journal of Computer Science and Technology 7, no. 4 (2024): 195–217. https://doi.org/10.11648/j.ajcst.20240704.17.

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The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems
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Wang, Pin, Yongming Li, Bohan Chen, et al. "Proportional Hybrid Mechanism for Population Based Feature Selection Algorithm." International Journal of Information Technology & Decision Making 16, no. 05 (2017): 1309–38. http://dx.doi.org/10.1142/s0219622014500096.

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Feature selection is an important research field for pattern classification, data mining, etc. Population-based optimization algorithms (POA) have high parallelism and are widely used as search algorithm for feature selection. Population-based feature selection algorithms (PFSA) involve compromise between precision and time cost. In order to optimize the PFSA, the feature selection models need to be improved. Feature selection algorithms broadly fall into two categories: the filter model and the wrapper model. The filter model is fast but less precise; while the wrapper model is more precise b
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Chen, Chang Huang. "Fusing Multiple Strategies in Population-Based Optimization Algorithm." Applied Mechanics and Materials 764-765 (May 2015): 1407–11. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.1407.

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A multi-strategy based population optimization, referred to MSPO, is proposed in this paper. The algorithm is developed by hybridizing four different population-based algorithms, bare bone particle swarm optimization, quantum-behaved particle swarm optimization, differential evolution and opposition-based learning. It aims at enhancing the exploration and exploitation capability of population based algorithm for general optimization problem. These four options are randomly selected with equal probability during the search process. The proposed algorithm is validated against test functions and
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Liu, Zijian, Chunbo Luo, Peng Ren, Tingwei Wang, and Geyong Min. "Population based optimization via differential evolution and adaptive fractional gradient descent." Filomat 34, no. 15 (2020): 5173–85. http://dx.doi.org/10.2298/fil2015173l.

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We propose a differential evolution algorithm based on adaptive fractional gradient descent (DE-FGD) to address the defects of existing bio-inspired algorithms, such as slow convergence speed and local optimum. The crossover and selection processes of the differential evolution algorithm are discarded and the adaptive fractional gradients are adopted to enhance the global searching capability. For the benchmark functions, our proposed algorithm Specifically, our method has higher searching accuracy than several state of the art bio-inspired algorithms. Furthermore, we apply our method to speci
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Wang, Jun Wei, and Jing Hao. "Population Adaptive Immune Algorithm Based Trustworthy QoS Routing Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 647–52. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.647.

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The trustworthiness of data transmission is a very important parameter in the future network. In this paper, considering the optimal ability of population adaptive immune and the trustworthiness demand of the network, a trustworthy QoS routing algorithm is proposed. The interval is used to describe the user requirement in order to adapt to the fuzziness of the user QoS and trust demand, and the sliding window is adopted to implement the trust evaluation and control mechanism. With satisfaction degree function introduced, it tries to find the optimal path which satisfies the user requirement ba
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FENG, Yanhong, Jianqin LIU, and Yichao HE. "Chaos-based dynamic population firefly algorithm." Journal of Computer Applications 33, no. 3 (2013): 796–99. http://dx.doi.org/10.3724/sp.j.1087.2013.00796.

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Feng, Xiaoqin, Rong Xie, Junyang Sheng, and Shuo Zhang. "Population Statistics Algorithm Based on MobileNet." Journal of Physics: Conference Series 1237 (June 2019): 022045. http://dx.doi.org/10.1088/1742-6596/1237/2/022045.

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Dissertations / Theses on the topic "Population-based algorithm"

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Nilsson, Mikael. "Parameter Tuning Experiments of Population-based Algorithms." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-13836.

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In this study, three different algorithms are implemented to solve thecapacitated vehicle routing problem with and without time windows:ant colony optimization, a genetic algorithm and a genetic algorithmwith self-organizing map. For the capacitated vehicle routing problemthe Augerat et al’s benchmark problems were used and for the capaci-tated vehicle routing problem with time windows the Solomon’sbenchmark problems. All three algorithms were tuned over thirtyinstances per problem with the tuners SPOT and ParamILS. The tuningresults from all instances were combined to the final parameter valu
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Vuckovich, David N. "An open-source population indifference zone-based algorithm for simulation optimization." Connect to resource, 2009. http://hdl.handle.net/1811/36949.

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Liao, Tianjun. "Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209439.

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Continuous optimization problems are optimization problems where all variables<p>have a domain that typically is a subset of the real numbers; mixed discrete-continuous<p>optimization problems have additionally other types of variables, so<p>that some variables are continuous and others are on an ordinal or categorical<p>scale. Continuous and mixed discrete-continuous problems have a wide range<p>of applications in disciplines such as computer science, mechanical or electrical<p>engineering, economics and bioinformatics. These problems are also often hard to<p>solve due to their inherent diffi
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Siqueira, Gomes Hugo. "Meta learning for population-based algorithms in black-box optimization." Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/68764.

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Les problèmes d’optimisation apparaissent dans presque tous les domaines scientifiques. Cependant, le processus laborieux de conception d’un optimiseur approprié peut demeurer infructueux. La question la plus ambitieuse de l’optimisation est peut-être de savoir comment concevoir des optimiseurs suffisamment flexibles pour s’adapter à un grand nombre de scénarios, tout en atteignant des performances de pointe. Dans ce travail, nous visons donner une réponse potentielle à cette question en étudiant comment faire un méta-apprentissage d’optimiseurs à base de population. Nous motivons et décrivons
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al-Rifaie, Mohammad Majid. "Information sharing impact of stochastic diffusion search on population-based algorithms." Thesis, Goldsmiths College (University of London), 2011. http://research.gold.ac.uk/6708/.

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This work introduces a generalised hybridisation strategy which utilises the information sharing mechanism deployed in Stochastic Diffusion Search when applied to a number of population-based algorithms, effectively merging this nature-inspired algorithm with some population-based algorithms. The results reported herein demonstrate that the hybrid algorithm, exploiting information-sharing within the population, improves the optimisation capability of some well-known optimising algorithms, including Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm. This hybrid
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Hajizadeh, Yasin. "Population-based algorithms for improved history matching and uncertainty quantification of petroleum reservoirs." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2423.

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In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a
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Montero, Elisabeth. "Calibration strategies for bio-inspired population-based algorithms that solve combinatorial optimization problems." Nice, 2011. http://www.theses.fr/2011NICE4040.

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La sélection de valeurs adéquates pour les paramètres des algorithmes bio-inspirés est un processus crucial pour obtenir de bonnes performances. C’est un processus complexe étant donné que les valeurs des paramètres dépendent en général du problème que l’on cherche à résoudre. Il est de plus très coûteux en temps, et les paramètres ne sont pas complètement indépendants entre eux. La première contribution de cette thèse consiste en une étude comparative des caractéristiques et performances des différentes méthodes connues dans la littérature pour ajuster les paramètres, en analysant leurs avant
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Ras, Marthinus Nicolaas. "The development of some rotationally invariant population based optimization methods." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80172.

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Thesis (MScEng)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: In this study we consider the lack of rotational invariance of three different population based optimization methods, namely the particle swarm optimization (PSO) algorithm, the differential evolution (DE) algorithm and the continuous-parameter genetic algorithm (CPGA). We then propose rotationally invariant versions of these algorithms. We start with the PSO. The so-called classical PSO algorithmis known to be variant under rotation, whereas the linear PSO is rotationally invariant. This invariance however, comes at the
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Khouadjia, Mostepha Redouane. "Solving dynamic vehicle routing problems : from single-solution based metaheuristics to parallel population based metaheuristics." Thesis, Lille 1, 2011. http://www.theses.fr/2011LIL10140/document.

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Beaucoup de problèmes dans le monde réel ont une nature dynamique et peuvent être modélisés comme des problèmes dynamiques d'optimisation combinatoire. Cependant, les travaux de recherches sur l'optimisation dynamique se concentrent essentiellement sur les problèmes d'optimisation continue et ils ciblent rarement les problèmes combinatoires. Une des applications dans le domaine des problèmes dynamiques combinatoires ayant reçu un intérêt croissant au cours de ces dernières décennies est le système de transport en ligne où dynamique. Un problème typique de ce domaine est le Problème Dynamique d
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Riahi, Vahid. "Constraint Directed Scheduling." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386545.

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Scheduling is a decision-making process, which is employed to allocate resources to tasks in a given time. Scheduling problems are in general NP-hard. In order to solve scheduling problems, three common types of methods have been used: exact methods (e.g., branch & bound and dynamic programming), population based metaheuristics (e.g., genetic algorithm and ant colony optimisation), and local search (LS) algorithms (e.g., simulated annealing and iterated local search). Exact methods are not able to address the practical-sized problems effectively with regard to both CPU times and solution quali
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Books on the topic "Population-based algorithm"

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Sokol'skaya, Elena, and Boris Kochurov. Geoecology of the city: models of environmental quality. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1205961.

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The monograph examines the features of studying the geoecological state of urbanized territories, reveals the use of integrated assessment and mapping in urban diagnostics, and finds a solution to geoecological problems on the example of world cities that are leading in the rating for quality of life.&#x0D; The components of an information and analytical model of the urban environment for assessing the geoecological situation are described; an algorithm for a comprehensive study of the geoecological state aimed at an adequate assessment of the quality of the urban environment. Special attentio
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Crichton, Michael. Prey. Harper, 2008.

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Crichton, Michael. Prey. HarperCollins Publishers, 2003.

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Crichton, Michael. La proie. Robert Laffont, 2005.

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Crichton, Michael. La proie. R. Laffont, 2003.

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Crichton, Michael. Prey. HarperCollins, 2002.

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Crichton, Michael. Prey. Avon, 2003.

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Crichton, Michael. Presa. Debolsillo, 2005.

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Crichton, Michael. Prey. HarperCollins, 2002.

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Crichton, Michael. Prey. Harper Large Print, 2002.

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Book chapters on the topic "Population-based algorithm"

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Sharma, Tarun Kumar, and Millie Pant. "Intermediate Population Based Differential Evolution Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_24.

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Łapa, Krystian, Krzysztof Cpałka, Andrzej Przybył, and Konrad Grzanek. "Negative Space-Based Population Initialization Algorithm (NSPIA)." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91253-0_42.

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Chen, Yu, Kai Zhang, and Xiufen Zou. "A Population-Based Hybrid Extremal Optimization Algorithm." In Bio-Inspired Computing and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_54.

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Zhu, Kenny Q., and Ziwei Liu. "Population Diversity in Permutation-Based Genetic Algorithm." In Machine Learning: ECML 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_49.

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Ahmad, Waseem, and Ajit Narayanan. "Population-Based Artificial Immune System Clustering Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22371-6_30.

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Pooja, Praveena Chaturvedi, and Pravesh Kumar. "Population Segmentation-Based Variant of Differential Evolution Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0451-3_37.

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Lehre, Per Kristian, and Phan Trung Hai Nguyen. "Level-Based Analysis of the Population-Based Incremental Learning Algorithm." In Parallel Problem Solving from Nature – PPSN XV. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99259-4_9.

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Chen, Junfeng, Yingjuan Xie, and Hua Chen. "A Population-Based Extremal Optimization Algorithm with Knowledge-Based Mutation." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11857-4_11.

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Sine Cosine Algorithm." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_2.

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AbstractSine cosine algorithm (SCA) [1] is relatively a new algorithm, in the field of meta-heuristic algorithms. SCA is a population-based probabilistic search method that updates the position of search agents in the population using simple concept of trigonometric functions sine and cosine. SCA algorithm is inspired from the periodic property of the sine and cosine functions. The periodicity of the sine and cosine function in the range $$[-1,1]$$ provides great capacity to exploit the search space and helps in maintaining a fine balance between exploration and exploitation. In previous Chap.
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Lwin, Ko Ko, and Yuji Murayama. "Accuracy Assessment of GIS Based Building Population Estimation Algorithm." In Spatial Analysis and Modeling in Geographical Transformation Process. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0671-2_7.

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Conference papers on the topic "Population-based algorithm"

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Yuan, Dongfeng, and Qixun An. "A novel differential evolutionary algorithm based on population feedback information." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049806.

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Cao, Yang, Xingyu Wang, and Zhonghua Han. "Multi-population artificial bee colony algorithm based on Lagrange interpolation." In 2024 4th International Conference on Communication Technology and Information Technology (ICCTIT). IEEE, 2024. https://doi.org/10.1109/icctit64404.2024.10928391.

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Bai, Hao, Qingshan Lu, Tegang Lu, Wufan Sun, and Sheng Zhao. "Optimization method for ground-based air defense deployment based on multi-population genetic algorithm." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049601.

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Wang, Yuhan, Han Yu, Jiahui Ma, Nanping Li, and Lin Wang. "A Cross Strategy-Based Agent-Assisted Multi-Population Particle Swarm Optimization Algorithm." In 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS). IEEE, 2025. https://doi.org/10.1109/asens64990.2025.11011206.

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Yao, Jiaqi, and Bin Liu. "Many-Objective Evolutionary Algorithm Based on Spectral Clustering and Population Distribution." In 2024 International Conference on New Trends in Computational Intelligence (NTCI). IEEE, 2024. https://doi.org/10.1109/ntci64025.2024.10776165.

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Zhao, Shulin, Xingxing Hao, Li Chen, and Yahui Feng. "Multi-Population Constrained Multi-Objective Evolutionary Algorithm Based on Knowledge Transfer." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704519.

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Song, XiaoYu, Wen Li, and Ming Zhao. "ABC-DE hybrid evolutionary algorithm based on individual preference population selection." In Fifth International Conference on Computer Communication and Network Security (CCNS 2024), edited by Zhiyong Xiong and Zhaolong Ning. SPIE, 2024. http://dx.doi.org/10.1117/12.3038116.

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Patil, Sidagouda Basagouda, and Mukund A. Kulkarni. "Symmetric Adaptive Population Division Based Salp Swarm Algorithm for Resource Optimization in Cloud Computing." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721697.

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Smith, Gregory C., and Shiang-Fong Chen. "Automated Initial-Population Generation for Genetic-Algorithm-Based Assembly Planning." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/flex-14464.

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Abstract Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present genetic-algorithm-based mechanical-product assembly planners. However, genetic-algorithm-based assembly planners require an initial assembly-sequence population, and search efficiency greatly depends upon input-population quality. State-of-the-art genetic-algorithm-based assembly planners use one of two techniques for generating an initial assembly-sequence population: use a user-supplied assembly-sequence set or use a randomly generated assembly-sequence se
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Qingbin, Zhang, Wu Tihua, and Liu Bo. "An Improved Population-Based Incremental Learning Algorithm." In 2007 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.4347126.

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Reports on the topic "Population-based algorithm"

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Guliashki, Vassil, and Leoneed Kirilov. Algorithm Generating Initial Population of Schedules for Population-based Algorithms Solving Flexible Job Shop Problems. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.06.01.

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Tipton, Kelley, Brian F. Leas, Emilia Flores, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-se
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wave
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Bouillon, César P., and Patricia Yáñez-Pagans. Dynamic Consistency of Multidimensional and Income Targeting: An Application for Mexico Using Panel Data Information. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0011192.

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This paper compares the dynamic consistency of targeting methodologies that use multidimensional welfare indicators with those based on means and proxy means tests using panel data from Mexico. To make these comparisons, an extension of the Alkire and Foster (2008) dual cutoff multidimensional poverty methodology is proposed. This extension provides a relative approach to multidimensional deprivation that ranks individuals according to an aggregate of their relative position in the distribution of a set of welfare attributes or outcomes. The extension gives particular importance to deprivation
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Rankin, Nicole, Deborah McGregor, Candice Donnelly, et al. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, 2019. http://dx.doi.org/10.57022/clzt5093.

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Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asy
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule se
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A Guidebook on Mapping Poverty through Data Integration and Artificial Intelligence. Asian Development Bank, 2021. http://dx.doi.org/10.22617/spr210131-2.

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The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators to be estimated for different segments of a country’s population. The Asian Development Bank, in collaboration with the Philippine Statistics Authority, the National Statistical Office of Thailand, and the World Data Lab, conducted a feasibility study that aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines and Thailand. This accompanying guide to the Key Indicators for Asia and the Pacific 2020 special supple
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Kenya: Identifying RTIs remain problematic: Prevention is essential. Population Council, 2000. http://dx.doi.org/10.31899/rh2000.1015.

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Since 1990, the Nakuru Municipal Council (Kenya) has implemented a multifaceted program to reduce the incidence of reproductive tract infections (RTIs), especially those that are sexually transmitted, including HIV/AIDS. Staff in the Council’s five health clinics use syndromic management guidelines, based on clients’ reported symptoms and clinical signs, to identify clients with RTIs. In 1998, the Population Council conducted a study to assess the accuracy of syndromic management and determine the best ways to integrate RTI management into existing antenatal (ANC) and family planning (FP) serv
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9

Payment Systems Report - June of 2021. Banco de la República, 2022. http://dx.doi.org/10.32468/rept-sist-pag.eng.2021.

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Banco de la República provides a comprehensive overview of Colombia’s finan¬cial infrastructure in its Payment Systems Report, which is an important product of the work it does to oversee that infrastructure. The figures published in this edition of the report are for the year 2020, a pandemic period in which the con¬tainment measures designed and adopted to alleviate the strain on the health system led to a sharp reduction in economic activity and consumption in Colom¬bia, as was the case in most countries. At the start of the pandemic, the Board of Directors of Banco de la República adopted
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