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

Group learning algorithm: a new metaheuristic algorithm

Rahman, C. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08465-5 · Cited by 31

Konvolüsyonel Sinir Ağlarında Hiper-Parametre Optimizasyonu Yöntemlerinin İncelenmesi

Gülcü, A., & Kuş, Z. (2019) · Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji · doi:10.29109/gujsc.514483 · Cited by 34

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

Qaraad, M., Amjad, S., Hussein, N. K., Mirjalili, S., Halima, N. B., & Elhosseini, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3202894 · Cited by 30

Homogenization of Daily Temperature Data

Hewaarachchi, A. P., Li, Y., Lund, R., & Rennie, J. (2016) · Journal of Climate · doi:10.1175/jcli-d-16-0139.1 · Cited by 30

High-throughput Ant Colony Optimization on graphics processing units

Cecilia, J. M., Llanes, A., Abellán, J. L., Gómez-Luna, J., Chang, L.-W., & Hwu, W. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.12.002 · Cited by 31

Multiple-order permutation flow shop scheduling under process interruptions

Rahman, H. F., Sarker, R. A., & Essam, D. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-2146-z · Cited by 30

Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm

Yang, X., Long, J., Liu, P., Zhang, X., & Liu, X. (2018) · Energies · doi:10.3390/en11092381 · Cited by 30

Operational reliability of multi-energy customers considering service-based self-scheduling

Wang, S., Shao, C., Ding, Y., & Yan, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113531 · Cited by 30

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi, E., García-Arce, A., Masel, D. T., Reich, E., Puckey, J., & Maff, R. M. (2019) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2019.1649764 · Cited by 31

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Hu, Z., Bai, Z., Bian, K., Wang, T., & Song, L. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2900751 · Cited by 30

Multi-area economic dispatch with stochastic wind power using Salp Swarm Algorithm

Chaudhary, V., Dubey, H. M., Pandit, M., & Bansal, J. C. (2020) · Array · doi:10.1016/j.array.2020.100044 · Cited by 31

An integrated approach for enhancing the overall performance of constructed wetlands in urban areas

Liao, R., Jin, Z., Chen, M., & Li, S. (2020) · Water Research · doi:10.1016/j.watres.2020.116443 · Cited by 29

A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

Abdel‐Basset, M., Mohamed, R., Abouhawwash, M., Chakrabortty, R. K., & Ryan, M. J. (2021) · Mathematics · doi:10.3390/math9030270 · Cited by 30

RAI: Rapid, Autonomous and Intelligent machine learning approach to identify fire-vulnerable bridges

Abedi, M., & Naser, M. Z. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107896 · Cited by 30

Optimization of Surgical Robotic Instrument Mounting in a Macro–Micro Manipulator Setup for Improving Task Execution

Cursi, F., Bai, W., Yeatman, E. M., & Kormushev, P. S. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3171097 · Cited by 30

A node selection algorithm with a genetic method based on PBFT in consortium blockchains

Zhang, J., Yang, Y., Zhao, D., & Wang, Y. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00907-2 · Cited by 32

Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory

Yu, H., Wang, Y., Chen, S., Fei, W., Weng, C., Zhao, J., & Wei, Z. (2014) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2014.6742888 · Cited by 30

Impact of Metaheuristic Iteration on Artificial Neural Network Structure in Medical Data

Salman, I., Osman Nuri, U. C. A. N., Bayat, O., & Shaker, K. (2018) · Processes · doi:10.3390/pr6050057 · Cited by 30

Branch coverage prediction in automated testing

Grano, G., Titov, T. V., Panichella, S., & Gall, H. C. (2019) · Journal of Software Evolution and Process · doi:10.1002/smr.2158 · Cited by 30

A Knowledge-Intensive Genetic Algorithm for Supervised Learning

Janikow, C. Z. (1993) · Springer eBooks · doi:10.1007/978-1-4615-2740-4_3 · Cited by 30

An efficient resource allocation of IoT requests in hybrid fog–cloud environment

Afzali, M., Samani, A. M. V., & Naji, H. R. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05586-5 · Cited by 29

An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

Abdulsalami, A. O., Elaziz, M. A., Gharehchopogh, F. S., Salawudeen, A. T., & Xiong, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111351 · Cited by 29

Business intelligence to stimulate the commercial turnaround in the microcenter of an intermediate-sized city

Debortoli, D. O., & Brignole, N. B. (2024) · Región Científica · doi:10.58763/rc2024195 · Cited by 29

An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification

Abubacker, N. F., Azman, A., Doraisamy, S. C., & Murad, M. A. A. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2290-z · Cited by 30

Polarization-independent two-dimensional diffraction metal-dielectric grating

Chen, J., Zhang, Y.-B., Wang, Y., Kong, F., Jin, Y., Chen, P., Xu, J., Sun, S., & Shao, J. (2018) · Applied Physics Letters · doi:10.1063/1.5040729 · Cited by 29

Calibration of water quality model for distribution networks using genetic algorithm, particle swarm optimization, and hybrid methods

Peirovi-Minaee, R., Afsharnia, M., Moghaddam, A., Ebrahimi, A. A., Askarishahi, M., & Mokhtari, M. (2019) · MethodsX · doi:10.1016/j.mex.2019.03.008 · Cited by 29

Detecting explosives by PGNAA using KNN Regressors and decision tree classifier: A proof of concept

Hossny, K., Magdi, S., Soliman, A. Y. A., & Hossny, A. H. (2020) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2020.103332 · Cited by 29

Robust aerodynamic morphing shape optimization for high-lift missions

Nemati, M., & Jahangirian, A. (2020) · Aerospace Science and Technology · doi:10.1016/j.ast.2020.105897 · Cited by 29

A data-driven method for detecting and diagnosing causes of water quality contamination in a dataset with a high rate of missing values

Ngouna, R. H., Ratolojanahary, R., Medjaher, K., Dauriac, F., Sébilo, M., & Junca-Bourié, J. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103822 · Cited by 29

Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation

Ray, S., Das, A., Dhal, K. G., Gálvez, J., & Naskar, P. K. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05368-7 · Cited by 30

Assessment of Soil Thermal Conductivity Based on BPNN Optimized by Genetic Algorithm

Liu, C., Hu, X., Ren, Y., Han, Y., Wang, Y., He, W., Fan, H., & Du, L. (2020) · Advances in Civil Engineering · doi:10.1155/2020/6631666 · Cited by 29

Toward Automated Exploit Generation for Known Vulnerabilities in Open-Source Libraries

Iannone, E., Nucci, D. D., Sabetta, A., & Lucia, A. D. (2021) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc52881.2021.00046 · Cited by 29

Automatic and intelligent content visualization system based on deep learning and genetic algorithm

İnce, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06887-1 · Cited by 31

Self-adaptive salp swarm algorithm for optimization problems

Kassaymeh, S., Abdullah, S., Al‐Betar, M. A., Alweshah, M., Al-Laham, M., & Othman, Z. (2022) · Soft Computing · doi:10.1007/s00500-022-07280-9 · Cited by 29

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

Balakrishnan, K., Dhanalakshmi, R., & Seetharaman, G. (2022) · Expert Systems · doi:10.1111/exsy.13079 · Cited by 29

Predicting steady-state biogas production from waste using advanced machine learning-metaheuristic approaches

Sun, Y., Dai, H.-L., Moayedi, H., Le, B. N., & Adnan, R. M. (2023) · Fuel · doi:10.1016/j.fuel.2023.129493 · Cited by 29

A hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems

Li, J., Pan, Q.-K., & Xie, S. (2010) · Computer Science and Information Systems · doi:10.2298/csis090608017l · Cited by 29

Model-Driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity

Antonietti, A., Casellato, C., D'Angelo, E. U., & Pedrocchi, A. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2598190 · Cited by 29

AEkNN: An AutoEncoder kNN–Based Classifier With Built-in Dimensionality Reduction

Pulgar, F. J., Charte, F., Rivera, A. J., & del Jesús, M. J. (2019) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.2018.125905686 · Cited by 31

Advanced Multi-Objective Robust Optimization Under Interval Uncertainty Using Kriging Model and Support Vector Machine

Xie, T., Jiang, P., Zhou, Q., Shu, L., Zhang, Y., Meng, X., & Hua, W. D. (2018) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4040710 · Cited by 29

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

Hameed, S. S., Muhammadsharif, F. F., hassan, R. B., & Saeed, F. (2018) · Journal of Computer Science · doi:10.3844/jcssp.2018.868.880 · Cited by 29

Induction of a marsupial density model using genetic programming and spatial relationships

Whigham, P. A. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00248-9 · Cited by 29

A genetic-based machine learning system to discover the diagnostic rules for female urinary incontinence

Laurikkala, J. P. S., & Juhola, M. (1998) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(97)00067-9 · Cited by 29

Parameter Estimation in Land Surface Models: Challenges and Opportunities With Data Assimilation and Machine Learning

Raoult, N., Douglas, N., MacBean, N., Kolassa, J., Quaife, T. L., Roberts, A. G., Fisher, R. A., Fer, I., Bacour, C., Dagon, K., et al. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2024ms004733 · Cited by 47

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Holzinger, K., Palade, V., Rabadán, R., & Holzinger, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43968-5_3 · Cited by 28

Step-wise support vector machines for classification of overlapping samples

Fu, M., Tian, Y., & Wu, F. J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.12.035 · Cited by 28

Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Vyas, R., Bapat, S., Goel, P., Karthikeyan, M., Tambe, S. S., & Kulkarni, B. D. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2621042 · Cited by 28

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification

Taghanaki, S. A., Kawahara, J., Miles, B., & Hamarneh, G. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.04.012 · Cited by 28

Focus Group: An Optimization Algorithm Inspired by Human Behavior

Fattahi, E., Bidar, M., & Kanan, H. R. (2018) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026818500025 · Cited by 28

A stochastic multiscale algorithm for modeling complex granular materials

Tahmasebi, P., & Sahimi, M. (2018) · Granular Matter · doi:10.1007/s10035-018-0816-z · Cited by 28

Optimum stacking sequence design of composite laminates for maximum buckling load capacity using parameter-less optimization algorithms

Kaveh, A., Eslamlou, A. D., & Malek, N. G. (2018) · Engineering With Computers · doi:10.1007/s00366-018-0634-2 · Cited by 28

A method to optimize mobile crane and crew interactions to minimize construction cost and time

Peng, B., Flager, F., & Wu, J. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.07.015 · Cited by 28

Empirical Study of Sperm Swarm Optimization Algorithm

Shehadeh, H. A., Ahmedy, I., & Idris, M. Y. I. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01057-7_80 · Cited by 28

The feasibility and optimization of biodiesel production from Celtis australis L. oil using chicken bone catalyst and ultrasonic waves

Samani, M. A., Samani, B. H., Dehkordi, A. L., Rostami, S., Najafi, G., Fayyazi, E., & Mamat, R. (2019) · Biofuels · doi:10.1080/17597269.2019.1628482 · Cited by 28

A new ensemble feature selection approach based on genetic algorithm

Wang, H., He, C., & Li, Z. (2020) · Soft Computing · doi:10.1007/s00500-020-04911-x · Cited by 28

Evolutionary Techniques for Optimizing Air Quality Model

Bhardwaj, R., & Pruthi, D. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.206 · Cited by 28

Analysis and implementation of fractional-order chaotic system with standard components

Yao, J., Wang, K., Huang, P., Chen, L., & Machado, J. A. T. (2020) · Journal of Advanced Research · doi:10.1016/j.jare.2020.05.008 · Cited by 28

Efficient butterfly inspired optimization algorithm for analog circuits design

Lberni, A., Marktani, M. A., Ahaitouf, A., & Ahaitouf, A. (2021) · Microelectronics Journal · doi:10.1016/j.mejo.2021.105078 · Cited by 28

A multi-objective evolutionary algorithm based on length reduction for large-scale instance selection

Cheng, F., Chu, F., & Zhang, L. (2021) · Information Sciences · doi:10.1016/j.ins.2021.06.052 · Cited by 28

Wrapper feature selection with partially labeled data

Feofanov, V., Devijver, É., & Amini, M.-R. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03076-w · Cited by 29

A constrained locking sweeping method and velocity obstacle based path planning algorithm for unmanned surface vehicles in complex maritime traffic scenarios

Su, Y., Luo, J., Zhuang, J., Song, S., Huang, B., & Zhang, L. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113538 · Cited by 28

A multivariate algorithm for identifying contaminated peanut using visible and near-infrared hyperspectral imaging

Guo, Z., Zhang, J., Sun, J., Dong, H., Huang, J., Geng, L., Li, S., Jing, X., Guo, Y., & Sun, X. (2023) · Talanta · doi:10.1016/j.talanta.2023.125187 · Cited by 28

A variegated GWO algorithm implementation in emerging power systems optimization problems

Dey, B., Raj, S., Mahapatra, S., & Márquez, F. P. G. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107574 · Cited by 28

Multiobjective optimized bipedal locomotion

Raj, M., Semwal, V. B., & Nandi, G. C. (2017) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0660-1 · Cited by 28

Topology-optimized dual-polarization Dirac cones

Lin, Z., Christakis, L., Li, Y., Mazur, E., Rodríguez, A. W., & Lončar, M. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.97.081408 · Cited by 26

Elephant quantum walk

Molfetta, G. D., Soares-Pinto, D. O., & Queirós, S. M. D. (2018) · Physical Review A · doi:10.1103/physreva.97.062112 · Cited by 29

Evolutionary Network Embedding Preserving both Local Proximity and Community Structure

Li, M., Liu, J., Wu, P., & Teng, X. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2937455 · Cited by 28

Illuminating elite patches of chemical space

Verhellen, J., & den Abeele, J. V. (2020) · Chemical Science · doi:10.1039/d0sc03544k · Cited by 31

Automated Classification of Airborne Laser Scanning Point Clouds

Waldhauser, C., Hochreiter, R., Otepka, J., Pfeifer, N., Ghuffar, S., Korzeniowska, K., & Wagner, G. (2014) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-08985-0_12 · Cited by 28

A recommender system based on genetic algorithm for music data

Kim, H.-T., Kim, E., Lee, J., & Ahn, C. W. (2010) · International Conference on Computer Engineering and Technology · doi:10.1109/iccet.2010.5486161 · Cited by 28

A review on technological aspects of different PWM techniques and its comparison based on different performance parameters

Chatterjee, S., & Das, A. K. (2022) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.3513 · Cited by 27

Prediction and reliability analysis of shear strength of RC deep beams

Megahed, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-64386-w · Cited by 30

Can big data tame a “naughty” world?

Salmond, J., Tadaki, M., & Dickson, M. E. (2017) · Canadian Geographies / Géographies canadiennes · doi:10.1111/cag.12338 · Cited by 27

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

Skakov, E., & Malysh, V. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/973/1/012063 · Cited by 27

Conventional, Intelligent, and Fractional-Order Control Method for Maximum Power Point Tracking of a Photovoltaic System: A Review

Kamal, N. A., & Ibrahim, A. M. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-816152-4.00020-0 · Cited by 27

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

Kim, D. I., Lee, S., & Shin, J. S. (2020) · IEEE Access · doi:10.1109/access.2020.2968918 · Cited by 27

Minimization of total harmonic distortions of cascaded H-bridge multilevel inverter by utilizing bio inspired AI algorithm

Salman, M., Haq, I. U., Ahmad, T., Ali, H., Qamar, A., Basit, A., Khan, M., & Iqbal, J. (2020) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-020-01686-5 · Cited by 27

COVID-19 rehabilitation units are twice as expensive as regular rehabilitation units

Iannaccone, S., Alemanno, F., Houdayer, E., Brugliera, L., Castellazzi, P., Cianflone, D., Meloni, C., Ambrosio, A. G., Mortini, P., Spina, A., et al. (2020) · Journal of Rehabilitation Medicine · doi:10.2340/16501977-2704 · Cited by 28

An ensemble topic extraction approach based on optimization clusters using hybrid multi-verse optimizer for scientific publications

Abasi, A. K., Khader, A. T., Al‐Betar, M. A., Naim, S., Alyasseri, Z. A. A., & Makhadmeh, S. N. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02439-4 · Cited by 27

Application of mutation operators to salp swarm algorithm

Salgotra, R., Singh, U., Singh, G., Singh, S., & Gandomi, A. H. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114368 · Cited by 27

Optimal transmission angle and dynamic balancing of slider-crank mechanism with joint clearance using Pareto Bi-objective Genetic Algorithm

Etesami, G., Felezi, M. E., & Nariman-zadeh, N. (2021) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-021-02834-8 · Cited by 27

Modeling of gas viscosity at high pressure-high temperature conditions: Integrating radial basis function neural network with evolutionary algorithms

Rezaei, F., Jafari, S., Hemmati‐Sarapardeh, A., & Mohammadi, A. H. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109328 · Cited by 27

Transient stability improvement of wave energy conversion systems connected to power grid using anti-windup-coot optimization strategy

Mahdy, A., Hasanien, H. M., Helmy, W., Turky, R. A., & Aleem, S. H. E. A. (2022) · Energy · doi:10.1016/j.energy.2022.123321 · Cited by 27

Effective load balancing strategy using fuzzy golden eagle optimization in fog computing environment

Singh, S. P. (2022) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100766 · Cited by 27

Water-sediment-energy joint operation model of large-scale reservoir group for sediment-laden rivers

Chen, L., Hou, B., Zhan, T., Ge, L., Qin, Y., & Zhong, W. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.133271 · Cited by 27

Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithm

Nsiah, R. A., Mantey, S., & Ziggah, Y. Y. (2023) · Smart Construction and Sustainable Cities · doi:10.1007/s44268-023-00019-x · Cited by 30

Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm

Peng, J., Meng, F., & Ai, Y. (2013) · Bioresource Technology · doi:10.1016/j.biortech.2013.03.194 · Cited by 28

Improved hybrid optimization algorithm for 3D protein structure prediction

Zhou, C., Hou, C., Wei, X., & Zhang, Q. (2014) · Journal of Molecular Modeling · doi:10.1007/s00894-014-2289-2 · Cited by 27

Dolphin monitoring for enhancing metaheuristic algorithms: Layout optimization of braced frames

Kaveh, A., & Farhoudi, N. (2015) · Computers & Structures · doi:10.1016/j.compstruc.2015.11.012 · Cited by 27

Power sources coordination through multivariable linear parameter‐varying/ control with application to multi‐source electric vehicles

Nwesaty, W., Bratcu, A. I., & Sename, O. (2016) · IET Control Theory and Applications · doi:10.1049/iet-cta.2015.1163 · Cited by 27

Physically Consistent Modeling of Dike‐Induced Deformation and Seismicity: Application to the 2014 Bárðarbunga Dike, Iceland

Heimisson, E. R., & Segall, P. (2020) · Journal of Geophysical Research Solid Earth · doi:10.1029/2019jb018141 · Cited by 27

An innovative time-varying particle swarm-based Salp algorithm for intrusion detection system and large-scale global optimization problems

Qaraad, M., Amjad, S., Hussein, N. K., Mirjalili, S., & Elhosseini, M. A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10322-1 · Cited by 27

Protective Relaying Coordination in Power Systems Comprising Renewable Sources: Challenges and Future Insights

Agwa, A., & El-Fergany, A. (2023) · Sustainability · doi:10.3390/su15097279 · Cited by 24

Development and Demonstration of Routing and Placement EDA Tools for Large-Scale Adiabatic Quantum-Flux-Parametron Circuits

Murai, Y., Ayala, C. L., Takeuchi, N., Yamanashi, Y., & Yoshikawa, N. (2017) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2017.2721965 · Cited by 26

Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

Hazra, S., Pal, T., & Roy, P. K. (2018) · International Journal of Swarm Intelligence Research · doi:10.4018/ijsir.2019010103 · Cited by 26

An Adversarial Approach to Private Flocking in Mobile Robot Teams

Zheng, H., Panerati, J., Beltrame, G., & Prorok, A. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.49744 · Cited by 26

Design of tidal range energy generation schemes using a Genetic Algorithm model

Xue, J., Ahmadian, R., Jones, O., & Falconer, R. A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116506 · Cited by 26

Cooperative meta-heuristic algorithms for global optimization problems

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