Genetic Algorithms and Machine Learning

Genetic algorithms in search, optimization, and machine learning

(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,178

Adaptation in Natural and Artificial Systems

Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340

Genetic Algorithms in Search, Optimization and Machine Learning

Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391

Book Reviews : Steps to an Ecology of Mind. By GREGORY BATESON. (New York: Ballantine Books, 1972. Pp. xxvi, 517. $1.95.)

Foxman, D., & Bateson, G. (1973) · The Western Political Quarterly · doi:10.2307/446833 · Cited by 7,263

Steps to an Ecology of Mind

Bateson, G. (2000) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226924601.001.0001 · Cited by 5,573

Society of Mind

Seel, P. D. N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5794 · Cited by 2,566

Untitled work

(1987) · Medical Entomology and Zoology · Cited by 2,512

Induction: Processes of Inference, Learning, and Discovery

Holland, J. H., Holyoak, K. J., Nisbett, R. E., Thagard, P. R., & Smoliar, S. W. (1987) · IEEE Expert · doi:10.1109/mex.1987.4307100 · Cited by 2,305

Induction

Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986) · The MIT Press eBooks · doi:10.7551/mitpress/3729.001.0001 · Cited by 1,534

Outline for a Logical Theory of Adaptive Systems

Holland, J. H. (1962) · Journal of the ACM · doi:10.1145/321127.321128 · Cited by 1,117

An application of fuzzy linguistic summarization and fuzzy association rule mining to Kansei Engineering: a case study on cradle design

Akgül, E., Delice, Y., Aydoğan, E. K., & Boran, F. E. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03292-9 · Cited by 29

A two-step interval structural damage identification approach based on model updating and set-membership technique

Mo, J., Wang, L., & Gu, K. (2021) · Measurement · doi:10.1016/j.measurement.2021.109464 · Cited by 26

Artificial Intelligence in 3D Printing

Talaat, F. M., & Hassan, E. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-6129-4_6 · Cited by 26

A complex-valued encoding satin bowerbird optimization algorithm for global optimization

Zhang, S., Zhou, Y., & Luo, Q. (2019) · Evolving Systems · doi:10.1007/s12530-019-09307-3 · Cited by 26

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Cedro, L., Wieczorkowski, K., & Szcześniak, A. (2023) · Acta Mechanica et Automatica · doi:10.2478/ama-2024-0004 · Cited by 26

Optimal Battery Sizing in Photovoltaic Based Distributed Generation Using Enhanced Opposition-Based Firefly Algorithm for Voltage Rise Mitigation

Wong, L. A., Shareef, H., Mohamed, A., & Ibrahim, A. A. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/752096 · Cited by 26

Wind farm layout optimization by using Definite Point selection and genetic algorithm

Shakoor, R., Hassan, M. Y., Raheem, A., Rasheed, N., & Nasir, M. N. M. (2014) · IEEE International Conference on Power and Energy · doi:10.1109/pecon.2014.7062439 · Cited by 26

Software Defect Prediction Using Dynamic Support Vector Machine

Shuai, B., Li, H., Li, M., Zhang, Q., & Tang, C. (2013) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2013.61 · Cited by 26

Heterogeneous transfer learning for activity recognition using heuristic search techniques

Feuz, K., & Cook, D. J. (2014) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-03-2014-0020 · Cited by 27

Unified Design Strategies for Particulate Products

Peukert, W., Segets, D., Pflug, L., & Leugering, G. R. (2015) · Advances in chemical engineering · doi:10.1016/bs.ache.2015.10.004 · Cited by 26

Optimizing DMAs' formation in a water pipe network: the water aging and the operating pressure factors

Chondronasios, A., Gonelas, K., Kanakoudis, V., Patelis, M., & Korkana, P. (2017) · Journal of Hydroinformatics · doi:10.2166/hydro.2017.156 · Cited by 26

Dynamic group optimisation algorithm for training feed-forward neural networks

Tang, R., Fong, S. J., Deb, S., Vasilakos, A. V., & Millham, R. C. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.043 · Cited by 26

A Novel IoT Authorization Architecture on Hyperledger Fabric With Optimal Consensus Using Genetic Algorithm

Klaokliang, N., Teawtim, P., Aimtongkham, P., So–In, C., & Niruntasukrat, A. (2018) · ICT International Student Project Conference (ICT-ISPC) · doi:10.1109/ict-ispc.2018.8523942 · Cited by 27

Multi-Objective Volleyball Premier League algorithm

Moghdani, R., Salimifard, K., Demir, E., & Benyettou, A. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105781 · Cited by 26

Adaptive Exploration Artificial Bee Colony for Mathematical Optimization

Alsamia, S., Koch, E., Ghafil, H. N., & Ray, R. (2024) · AI · doi:10.3390/ai5040109 · Cited by 25

Intelligent secondary control in smart microgrids: an on-line approach for islanded operations

Shokoohi, S., Golshannavaz, S., Khezri, R., & Bevrani, H. (2018) · Optimization and Engineering · doi:10.1007/s11081-018-9382-9 · Cited by 25

Coke production scheduling problem: A parallel machine scheduling with batch preprocessings and location-dependent processing times

Liu, M., Chu, F., He, J., Yang, D., & Chu, C. (2018) · Computers & Operations Research · doi:10.1016/j.cor.2018.12.002 · Cited by 25

Prospect Theory-Based Optimal Bidding Model of a Prosumer in the Power Market

Wang, Y., Zhang, L., Ding, Q., & Zhang, K. (2020) · IEEE Access · doi:10.1109/access.2020.3011594 · Cited by 26

Teaching Learning-Based Optimization With Evolutionary Binarization Schemes for Tackling Feature Selection Problems

Thaher, T., Mafarja, M. M., Turabieh, H., Castillo, P. Á., Faris, H., & Aljarah, I. (2021) · IEEE Access · doi:10.1109/access.2021.3064799 · Cited by 26

A joint problem of strategic workforce planning and fleet renewal: With an application in defense

Turan, H. H., Jalalvand, F., Elsawah, S., & Ryan, M. J. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.04.010 · Cited by 25

Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation

Cockrell, C., & An, G. C. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.662845 · Cited by 25

Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Duan, Q., Wang, L., Kang, H., Shen, Y., Sun, X., & Chen, Q. (2021) · Symmetry · doi:10.3390/sym13061092 · Cited by 25

Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding

Ortega-Sánchez, N., Rodrí­guez-Esparza, E., Oliva, D. A., Pérez‐Cisneros, M., Mohamed, A. W., Dhiman, G., & Hernández‐Montelongo, R. (2021) · Soft Computing · doi:10.1007/s00500-021-06418-5 · Cited by 25

Agent-based simulation and simulation optimization approaches to energy planning under different scenarios: A hospital application case

Seçkiner, S. U., & Koç, A. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108163 · Cited by 25

Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study

Zanella, L., Facco, P., Bezzo, F., & Cimetta, E. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23169087 · Cited by 25

Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network

Dell’Annunziata, G. N., Arricale, V. M., Farroni, F., Genovese, A., Pasquino, N., & Tranquillo, G. (2022) · Sensors · doi:10.3390/s22239516 · Cited by 25

Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability

Joshua, C. J., Prassanna, J., Quadir, A., Sivaraman, A. K., & Tee, K. F. (2023) · Sustainability · doi:10.3390/su15064767 · Cited by 25

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Xu, Y., Zeng, M., Liu, Q.-H., & Wang, X. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/867149 · Cited by 25

An improved genetic algorithm for dynamic shortest path problems

Zhu, X., Luo, W., & Zhu, T. (2014) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2014.6900496 · Cited by 25

An effective power quality monitor placement method utilizing quantum-inspired particle swarm optimization

Ibrahim, A. A., Mohamed, A., Shareef, H., & Ghoshal, S. P. (2011) · doi:10.1109/iceei.2011.6021845 · Cited by 25

Competencias directivas requeridas por los CEO ante la complejidad de las organizaciones del siglo xxi

Marín, M. F., & Vanoni, G. (2016) · Suma de Negocios · doi:10.1016/j.sumneg.2016.02.005 · Cited by 29

A variant of environmental adaptation method with real parameter encoding and its application in economic load dispatch problem

Sharma, B., Prakash, R., Tiwari, S., & Mishra, K. K. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0900-9 · Cited by 25

Uncertainty Quantification of Microstructure—Governed Properties of Polysilicon MEMS

Mirzazadeh, R., & Mariani, S. (2017) · Micromachines · doi:10.3390/mi8080248 · Cited by 25

Inverting the design path for self-assembled block copolymers

Gadelrab, K., Hannon, A. F., Ross, C. A., & Alexander‐Katz, A. (2017) · Molecular Systems Design & Engineering · doi:10.1039/c7me00062f · Cited by 25

Comparison of Handcrafted Features and Deep Learning in Classification of Medical X-ray Images

Zare, M. R., Alebiosu, D. O., & Lee, S. L. (2018) · International Conference on Information Retrieval & Knowledge Management (CAMP) · doi:10.1109/infrkm.2018.8464688 · Cited by 27

Magnetic and electric Purcell factor control through geometry optimization of high index dielectric nanostructuresPreprint

Brûlé, Y., Wiecha, P. R., Cuche, A., Paillard, V., & Francs, G. C. D. (2022) · arXiv (Cornell University) · doi:10.1364/oe.460168 · Cited by 25

Barber Optimization Algorithm: A New Human-Based Approach for Solving Optimization Problems

Hamadneh, T., Batiha, B., Alsayyed, O., Aribowo, W., Montazeri, Z., Dehghani, M., Werner, F. M., Haider, A., Jawad, R. K., Ibraheem, I. K., et al. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.064087 · Cited by 24

Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map

Nikoo, M., Sadowski, Ł., Khademi, F., & Nikoo, M. R. (2017) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2017/3508189 · Cited by 24

Self-organized direction aware for regularized fuzzy neural networks

Souza, P. V. D. C., Nunes, C. F. G., Guimarães, A. J., Rezende, T. S., Araújo, V. S., & Arajuo, V. J. S. (2019) · Evolving Systems · doi:10.1007/s12530-019-09278-5 · Cited by 24

Cluster Gauss–Newton method

Aoki, Y., Hayami, K., Toshimoto, K., & Sugiyama, Y. (2020) · Optimization and Engineering · doi:10.1007/s11081-020-09571-2 · Cited by 24

Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning

Oluwasanmi, A., Aftab, M. U., Qin, Z., Son, N. T., Doan, T. V., Nguyen, S. B., Nguyen, S. H., & Nguyen, G. (2021) · Complexity · doi:10.1155/2021/5538927 · Cited by 25

An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

Zhang, C.-X., Zhou, K.-Q., Ye, S.-Q., & Zain, A. M. (2021) · IEEE Access · doi:10.1109/access.2021.3130640 · Cited by 24

Metaheuristic-based isolated microgrid sizing and uncertainty quantification considering EVs as shiftable loads

Mohseni, S., Khalid, R., & Brent, A. C. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.08.254 · Cited by 24

Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Hayat, I., Tariq, A., Shahzad, W., Masud, M., Ahmed, S., Ali, M. U., & Zafar, A. (2023) · Systems · doi:10.3390/systems11050221 · Cited by 24

Optimizing Modularization of Residential Housing Designs for Rapid Postdisaster Mass Production of Housing

Ghannad, P., & Lee, Y.-C. (2023) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002390 · Cited by 24

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Ravichandran, S., Manoharan, P., Jangir, P., & Selvarajan, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-42969-3 · Cited by 26

Artificial intelligence-based solution to estimate the spatial accuracy of volunteered geographic data

Mohammadi, N., & Malek, M. R. (2014) · Journal of Spatial Science · doi:10.1080/14498596.2014.927337 · Cited by 24

Automated segmentation of micro-CT images of bone formation in calcium phosphate scaffolds

Polak, S. J., Candido, S., Levengood, S. K. L., & Johnson, A. J. W. (2011) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.07.004 · Cited by 24

Combination of Expert Knowledge and a Genetic Fuzzy Inference System for Automatic Sleep Staging

Liang, S., Kuo, C., Shaw, F., Chen, Y.-H., Hsu, C.-H., & Chen, J.-Y. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2510365 · Cited by 24

Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

Seong, N.-C., Kim, J.-H., & Choi, W. (2019) · Sustainability · doi:10.3390/su11185122 · Cited by 24

Enhanced Salp Swarm Algorithm based on random walk and its application to training feedforward neural networks

Yin, Y., Tu, Q., & Chen, X. (2020) · Soft Computing · doi:10.1007/s00500-020-04832-9 · Cited by 24

Stacking sequence optimization of doubly-curved laminated composite shallow shells for maximum fundamental frequency by sequential permutation search algorithm

Zhao, J. N., Qin, S., Zhang, Y. J., Liang, K., & Li, X. (2021) · Computers & Structures · doi:10.1016/j.compstruc.2021.106560 · Cited by 24

A New Hybrid Particle Swarm Optimization–Teaching–Learning-Based Optimization for Solving Optimization Problems

Hubálovský, Š., Hubálovská, M., & Matoušová, I. (2023) · Biomimetics · doi:10.3390/biomimetics9010008 · Cited by 23

Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns

Megahed, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-85371-x · Cited by 23

Combining Fuzzy Logic and Genetic Algorithms to Optimize Cost, Time and Quality in Modern Agriculture

Erdoğdu, A., Dayı, F., YILDIZ, F., YANIK, A., & Ganji, F. (2025) · Sustainability · doi:10.3390/su17072829 · Cited by 29

Parameter estimation for Hammerstein control autoregressive systems using differential evolution

Mehmood, A., Aslam, M. S., Chaudhary, N. I., Zameer, A., & Raja, M. A. Z. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1317-6 · Cited by 23

Robust 2-DOF FOPID Controller Design for Maglev System Using Jaya Algorithm

Sain, D., Swain, S. K., Kumar, T., & Mishra, S. (2018) · IETE Journal of Research · doi:10.1080/03772063.2018.1496800 · Cited by 23

A sigmoid attractiveness based improved firefly algorithm and its applications in IIR filter design

Liu, A., Li, P., Deng, X., & Ren, L. (2020) · Connection Science · doi:10.1080/09540091.2020.1742660 · Cited by 23

A macroscopic soil-water transport model to simulate root water uptake in the presence of water and disease stress

Peddinti, S. R., Kambhammettu, B. V. N. P., Lad, R. S., Šimůnek, J., Gade, R. M., & Adinarayana, J. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124940 · Cited by 23

Complete Extraction of Protein Dynamics Information in Hydrogen/Deuterium Exchange Mass Spectrometry Data

Zhang, Z. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.9b05724 · Cited by 23

Investigating low-permeability sandstone based on physical experiments and predictive modeling

Chao, Z., Ma, G., He, K., & Wang, M. (2020) · Underground Space · doi:10.1016/j.undsp.2020.05.002 · Cited by 23

Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?

Agrawal, A., Yang, X., Agrawal, R., Yedida, R., Shen, X., & Menzies, T. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3073242 · Cited by 23

Cognitive Evaluation of Machine Learning Agents

Kadam, S., & Vaidya, V. G. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.11.003 · Cited by 24

An enhanced pathfinder algorithm for engineering optimization problems

Tang, C., Zhou, Y., Luo, Q., & Tang, Z. (2021) · Engineering With Computers · doi:10.1007/s00366-021-01286-x · Cited by 23

Scheduling project with stochastic durations and time-varying resource requests: A metaheuristic approach

Rahman, H. F., Chakrabortty, R. K., & Ryan, M. J. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107363 · Cited by 23

Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems

Xie, S., Wang, H., Kong, Y., & Hong, Y. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833776 · Cited by 24

Application of Genetic Algorithm‐Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model

Tapak, L., Afshar, S., Afrasiabi, M., Ghasemi, M. K., & Alirezaei, P. (2021) · BioMed Research International · doi:10.1155/2021/5520710 · Cited by 23

Cuckoo search with differential evolution mutation and Masi entropy for multi-level image segmentation

Ray, S., Parai, S., Das, A., Dhal, K. G., & Naskar, P. K. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11633-1 · Cited by 23

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

Kundu, T., Deepmala, D., & Jain, P. K. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02862-w · Cited by 23

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

Verma, P., & Parouha, R. P. (2022) · Soft Computing · doi:10.1007/s00500-022-07262-x · Cited by 23

Machine learning for wildfire classification: Exploring blackbox, eXplainable, symbolic, and SMOTE methods

al-Bashiti, ‬. K., & Naser, M. Z. (2022) · Natural Hazards Research · doi:10.1016/j.nhres.2022.08.001 · Cited by 23

Ensemble Learning Methods for Shear Strength Prediction of Fly Ash-Amended Soils with Lignin Reinforcement

Chen, W., Qu, S., Lin, L., Luo, Q., & Wang, T. (2023) · Journal of Materials in Civil Engineering · doi:10.1061/(asce)mt.1943-5533.0004676 · Cited by 24

Multi-agent planning and coordination for automated aircraft ground handling

Chen, S.-T., Ermiş, G., & Sharpanskykh, A. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104480 · Cited by 23

An effective multi-objective bald eagle search algorithm for solving engineering design problems

Zhang, Y., Zhou, Y., Zhou, G., & Luo, Q. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110585 · Cited by 23

Applying an artificial neural network to simulate and predict Chinese fir (Cunninghamia lanceolata) plantation carbon flux in subtropical China

Wen, X., Zhao, Z., Deng, X., Xiang, W., Dalun, T., Yan, W., Zhou, X., & Peng, C. H. (2014) · Ecological Modelling · doi:10.1016/j.ecolmodel.2014.09.006 · Cited by 22

Optimal battery placement in photovoltaic based distributed generation using binary firefly algorithm for voltage rise mitigation

Ai, W. L., Shareef, H., Ibrahim, A. A., & Mohamed, A. (2014) · IEEE International Conference on Power and Energy · doi:10.1109/pecon.2014.7062432 · Cited by 23

Extracting easy to understand summary using differential evolution algorithm

Nandhini, K., & Balasundaram, S. R. (2013) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2013.12.004 · Cited by 23

Closed-Form FIR Filter Design Based on Convolution Window Spectrum Interpolation

Huang, X., Jing, S., Wang, Z., Xu, Y., & Zheng, Y. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2494869 · Cited by 23

Multi-objective optimization for the geometry of trapezoidal corrugated morphing skins

Dayyani, I., & Friswell, M. I. (2016) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-016-1476-4 · Cited by 23

An effective change recommendation approach for supplementary bug fixes

Xia, X., & Lo, D. (2016) · Automated Software Engineering · doi:10.1007/s10515-016-0204-z · Cited by 23

Partial discharges and noise separation using spectral power ratios and genetic algorithms

Fresno, J. M., Rey, J. A. A., Martínez-Tarifa, J. M., & Robles, G. (2017) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2016.005898 · Cited by 23

Designing a camera placement assistance system for human motion capture based on a guided genetic algorithm

Aissaoui, A., Ouafi, A., Pudlo, P., Gillet, C., Baarir, Z.-E., & Ahmed, A. T. (2017) · Virtual Reality · doi:10.1007/s10055-017-0310-7 · Cited by 23

On comparison of tuning method of FOPID controller for controlling field controlled DC servo motor

Kumar, P., Chatterjee, S., Shah, D., Saha, U. K., & Chatterjee, S. (2017) · Cogent Engineering · doi:10.1080/23311916.2017.1357875 · Cited by 23

Optimizing HVAC operation in commercial buildings: A genetic algorithm multi-objective optimization framework

Papadopoulos, S., & Azar, E. (2016) · Winter Simulation Conference · doi:10.1109/wsc.2016.7822220 · Cited by 23

Chaotic enhanced colliding bodies algorithms for size optimization of truss structures

Kaveh, A., Eslamlou, A. D., & Montazeran, A. H. (2018) · Acta Mechanica · doi:10.1007/s00707-018-2149-8 · Cited by 23

A Non-Standard Characteristic Based Protection Scheme for Distribution Networks

Kılıçkıran, H. C., Akdemir, H., Şengör, İ., Kekezoğlu, B., & Paterakis, N. G. (2018) · Energies · doi:10.3390/en11051241 · Cited by 23

An Artificial Bee Colony-Based Green Routing Mechanism in WBANs for Sensor-Based E-Healthcare Systems

Yan, J. G., Peng, Y., Shen, D., Yan, X., & Deng, Q. (2018) · Sensors · doi:10.3390/s18103268 · Cited by 23

A Dynamic Traffic Assignment Model for the Sustainability of Pavement Performance

Mao, X., Wang, J., Yuan, C., Yu, W., & Gan, J. (2018) · Sustainability · doi:10.3390/su11010170 · Cited by 23

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Balande, U., & Shrimankar, D. D. (2019) · Mathematics · doi:10.3390/math7030250 · Cited by 23

Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

Ahn, W., Lee, H. S., Ryou, H., & Oh, K. J. (2020) · Sustainability · doi:10.3390/su12030849 · Cited by 23

A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling problem

Balande, U., & Shrimankar, D. D. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00487-5 · Cited by 23

Parallel multipopulation optimization for belief rule base learning

Zhu, W., Chang, L., Sun, J., Wu, G., Xu, X., & Xu, X. (2020) · Information Sciences · doi:10.1016/j.ins.2020.09.035 · Cited by 23

Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons

Sun, R., Fu, L., Wang, G., Cheng, Q., Hsu, L., & Ochieng, W. Y. (2021) · NAVIGATION Journal of the Institute of Navigation · doi:10.1002/navi.408 · Cited by 23

Contact map based crystal structure prediction using global optimization

Hu, J., Yang, W., Dong, R., Li, Y., Li, X., Li, S., & Siriwardane, E. M. D. (2021) · CrystEngComm · doi:10.1039/d0ce01714k · Cited by 23

Training Stronger Baselines for Learning to OptimizePreprint

Chen, T., Zhang, W., Zhou, J., Chang, S., Liu, S., Amini, L. D., & Wang, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09089 · Cited by 26

Systematic Review of the Time-Cost Optimization Models in Construction Management

ElSahly, O., Ahmed, S., & Abdelfatah, A. S. (2023) · Sustainability · doi:10.3390/su15065578 · Cited by 22

Multi-Objective Optimization of Morphology in High-Rise Residential Areas for Outdoor Thermal Comfort in Yulin City, Northwest China

Meng, Y., Hao, Y., Que, Y., Ren, J., & Liu, Y. (2024) · Buildings · doi:10.3390/buildings14061688 · Cited by 23

Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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