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

Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

Rodríguez, J. C. B., Torres, F. J., & Borrás, M.-D. (2020) · Energies · doi:10.3390/en13112761 · Cited by 40

Automated identification of libraries from vulnerability data

Chen, Y., Santosa, A. E., Sharma, A., & Lo, D. (2020) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1145/3377813.3381360 · Cited by 36

Deep reinforcement learning control of white-light continuum generation

Valensise, C. M., Giuseppi, A., Cerullo, G., & Polli, D. (2021) · Optica · doi:10.1364/optica.414634 · Cited by 36

Design and applications of an advanced hybrid meta-heuristic algorithm for optimization problems

Parouha, R. P., & Verma, P. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09962-6 · Cited by 36

Optimization of accelerated solvent extraction of ellagitannins in black raspberry seeds using artificial neural network coupled with genetic algorithm

Lee, G. E., Kim, R. H., Lim, T., Kim, J., Kim, S., Kim, H., & Hwang, K. T. (2022) · Food Chemistry · doi:10.1016/j.foodchem.2022.133712 · Cited by 36

Genetics-based machine learning for the assessment of certain neuromuscular disorders

Pattichis, C. S., & Schizas, C. N. (1996) · IEEE Transactions on Neural Networks · doi:10.1109/72.485678 · Cited by 36

Mechanical properties and thermal stability of ultrathin tungsten nanowires

Lin, K., Li, J.-Y., Lin, J.-S., Ju, S., Lü, J.-M., & Hsieh, J.-Y. (2013) · RSC Advances · doi:10.1039/c3ra46215c · Cited by 35

Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids

Mohseni, S., & Brent, A. C. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109172 · Cited by 35

A novel strategy for automatic test data generation using soft computing technique

Chawla, P., Chana, I., & Rana, A. (2015) · Frontiers of Computer Science · doi:10.1007/s11704-014-3496-9 · Cited by 36

Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery

Seoane, J. A., Campbell, C., Day, I. N. M., Casas, J. P., & Gaunt, T. R. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003876 · Cited by 34

Optimum design of laterally-supported castellated beams using tug of war optimization algorithm

Kaveh, A., & Shokohi, F. (2016) · STRUCTURAL ENGINEERING AND MECHANICS · doi:10.12989/sem.2016.58.3.533 · Cited by 35

Recognition of protein allosteric states and residues: Machine learning approaches

Zhou, H.-Y., Dong, Z., & Tao, P. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25218 · Cited by 36

Using Machine Learning to Identify Suicide Risk: A Classification Tree Approach to Prospectively Identify Adolescent Suicide Attempters

Hill, R. M., Oosterhoff, B. J., & Do, C. (2019) · Archives of Suicide Research · doi:10.1080/13811118.2019.1615018 · Cited by 35

Estimation of the hydraulic parameters of leaky aquifers based on pumping tests and coupled simulation/optimization: verification using a layered aquifer in Tianjin, China

Zheng, G., Ha, D., Loáiciga, H. A., Zhou, H., Zeng, C.-F., & Zhang, H. (2019) · Hydrogeology Journal · doi:10.1007/s10040-019-02021-z · Cited by 35

A fault diagnosis model of marine diesel engine cylinder based on modified genetic algorithm and multilayer perceptron

Hou, L., Zou, J., Du, C., & Zhang, J. (2019) · Soft Computing · doi:10.1007/s00500-019-04388-3 · Cited by 35

Can past failures help identify vulnerable bridges to extreme events? A biomimetical machine learning approach

Naser, M. Z. (2019) · Engineering With Computers · doi:10.1007/s00366-019-00874-2 · Cited by 35

Parametric optimization for morphing structures design: application to morphing wings adapting to changing flight conditions

Weaver-Rosen, J. M., Leal, P. B. C., Hartl, D. J., & Malak, R. (2020) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-020-02643-y · Cited by 35

Teaching-learning-based pathfinder algorithm for function and engineering optimization problems

Tang, C., Zhou, Y., Tang, Z., & Luo, Q. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02071-x · Cited by 35

Neuro-evolutionary approach for optimal selection of EEG channels in motor imagery based BCI application

Idowu, O. P., Adelopo, O., Ilesanmi, A. E., Li, X., Samuel, O. W., Fang, P., & Li, G. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102621 · Cited by 37

Nature and Biologically Inspired Image Segmentation Techniques

Singh, S., Mittal, N., Thakur, D., Singh, H., Oliva, D. A., & Demin, A. Y. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09619-1 · Cited by 35

Innovation lives in ecotones, not ecosystems

Ghazinoory, S., Phillips, F. Y., Afshari-Mofrad, M., & Bigdelou, N. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.06.067 · Cited by 39

Off-Grid Multi-Carrier Microgrid Design Optimisation: The Case of Rakiura–Stewart Island, Aotearoa–New Zealand

Mohseni, S., Brent, A. C., & Burmester, D. (2021) · Energies · doi:10.3390/en14206522 · Cited by 35

Credit Risk Evaluation: A Comprehensive Survey

Bhattacharya, A., Biswas, S. K., & Mandal, A. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13952-3 · Cited by 43

Two-machine flow shop scheduling integrated with preventive maintenance planning

Wang, S., & Liu, M. (2014) · International Journal of Systems Science · doi:10.1080/00207721.2014.900137 · Cited by 35

Effects of coupling anisotropic yield functions with the optimization process of extruded aluminum front rail geometries in crashworthiness

Kohar, C. P., Brahme, A. P., Imbert, J., Mishra, R. K., & Inal, K. (2017) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2017.08.026 · Cited by 35

A chance-constrained stochastic approach to intermodal container routing problems

Zhao, Y. X., Liu, R., Zhang, X., & Whiteing, A. E. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192275 · Cited by 35

Genetic algorithm for feature selection of EEG heterogeneous data

Saibene, A., & Gasparini, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119488 · Cited by 40

APPLICATION OF GREY WOLF OPTIMIZER IN DESIGN OF CASTELLATED BEAMS

Kaveh, A., & Shokohi, F. (2016) · Cited by 34

Estimation of permeability of naturally fractured reservoirs by pressure transient analysis: An innovative reservoir characterization and flow simulation

Azim, R. R. A., & Rahman, S. S. (2016) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2016.05.027 · Cited by 34

Modeling the change in particle size distribution in a gas-solid fluidized bed due to particle attrition using a hybrid artificial neural network-genetic algorithm approach

Farizhandi, A. A. K., Zhao, H., & Lau, R. W. M. (2016) · Chemical Engineering Science · doi:10.1016/j.ces.2016.08.015 · Cited by 35

Modified genetic algorithm based power allocation scheme for amplify-and-forward cooperative relay network

Gachhadar, A., Hindia, M. N., Qamar, F., Siddiqui, M. H. S., Noordin, K. A., & Amiri, I. S. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.04.022 · Cited by 34

Bio-inspired computational heuristics for Sisko fluid flow and heat transfer models

Raja, M. A. Z., Mehmood, A., Rehman, A. U., Khan, A., & Zameer, A. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.07.023 · Cited by 34

Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems

Kumar, A., Komaragiri, R., & Kumar, M. (2019) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.2667 · Cited by 34

Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

Masmoudi, M. A., Hosny, M., Demir, E., & Pesch, E. (2019) · Journal of Heuristics · doi:10.1007/s10732-019-09424-x · Cited by 34

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex

Barros-Zulaica, N., Rahmon, J., Chindemi, G., Perin, R., Markram, H., Ramaswamy, S., & Muller, E. B. (2019) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2019.00029 · Cited by 37

Mapping water salinity using Landsat-8 OLI satellite images (Case study: Karun basin located in Iran)

Ansari, M., & Akhoondzadeh, M. (2019) · Advances in Space Research · doi:10.1016/j.asr.2019.12.007 · Cited by 37

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning

Huang, J., Zhu, Y., Kelly, J. T., Jang, C., Wang, S., Xing, J., Chiang, P., Fan, S., Zhao, X., & Yu, L. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137701 · Cited by 34

Tuning genetic algorithm parameters using design of experiments

Mosayebi, M., & Sodhi, M. S. (2020) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3377929.3398136 · Cited by 34

Approximate Quantum Adders With Genetic Algorithms: An IBM Quantum Experience

Li, R., Álvarez Rodríguez, U., Lamata Manuel, L., & Solano Villanueva, E. L. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 34

Machine learning for vibrational spectroscopy via divide-and-conquer semiclassical initial value representation molecular dynamics with application to N-methylacetamide

Gandolfi, M., Rognoni, A., Aieta, C., Conte, R., & Ceotto, M. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0031892 · Cited by 34

A comparative study of nature-inspired metaheuristic algorithms using a three-phase hybrid approach for gene selection and classification in high-dimensional cancer datasets

Hameed, S. S., Hassan, W. H., Latiff, L. A., & Muhammadsharif, F. F. (2021) · Soft Computing · doi:10.1007/s00500-021-05726-0 · Cited by 34

Quantum Henry gas solubility optimization algorithm for global optimization

Mohammadi, D., Elaziz, M. A., Moghdani, R., Demir, E., & Mirjalili, S. (2021) · Engineering With Computers · doi:10.1007/s00366-021-01347-1 · Cited by 34

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Shehadeh, H. A., Mustafa, H. M. J., & Tubishat, M. (2021) · International Journal of Applied Metaheuristic Computing · doi:10.4018/ijamc.292507 · Cited by 34

Comparison and calibration of a real-time virtual stenting algorithm using Finite Element Analysis and Genetic Algorithms

Spranger, K., Capelli, C., Bosi, G. M., Schievano, S., & Ventikos, Y. (2015) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2015.03.022 · Cited by 34

EEG-Based Mild Depressive Detection Using Differential Evolution

Li, Y., Hu, B., Zheng, X., & Li, X. (2018) · IEEE Access · doi:10.1109/access.2018.2883480 · Cited by 34

Enjoyment recognition from physiological data in a car racing game

Tognetti, S., Garbarino, M., Bonanno, A. T., Matteucci, M., & Bonarini, A. (2010) · International Workshop on Affective Interaction in Natural Environments (AFFINE) · doi:10.1145/1877826.1877830 · Cited by 34

Cellular manufacturing design 1996–2021: a review and introduction to applications of Industry 4.0

YounesSinaki, R., Sadeghi, A., Mosadegh, H., Almasarwah, N., & Süer, G. A. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2105763 · Cited by 33

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

Alromema, N. A., Hassan, A., & Khan, T. (2023) · Diagnostics · doi:10.3390/diagnostics13040708 · Cited by 37

Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection

Hussein, N. K., Qaraad, M., Amjad, S., Farag, M. A., Hassan, S., Mirjalili, S., & Elhosseini, M. A. (2023) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwad053 · Cited by 33

Real-time monitoring of the corrosion behaviour of the 304SS in HCl solution using BPNN with joint image recognition and electrochemical noise

Zhang, H., Wu, Z., Chen, Y., Feng, K., Yan, H., Song, H., Luo, C., & Hu, Z. P. (2024) · Corrosion Science · doi:10.1016/j.corsci.2023.111779 · Cited by 33

An energy-efficient path planning method for unmanned surface vehicle in a time-variant maritime environment

Luo, J., Zhuang, J., Jin, M., Xu, F. Z., & Su, Y. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117544 · Cited by 33

MBHGA: A Matrix-Based Hybrid Genetic Algorithm for Solving an Agent-Based Model of Controlled Trade Interactions

Сумбатович, А. А. (2025) · IEEE Access · doi:10.1109/access.2025.3539460 · Cited by 47

Cooperation and collapse in a communal livestock production SES model – A case from South Africa

Rasch, S., Heckelei, T., Oomen, R., & Naumann, C. (2015) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.12.008 · Cited by 33

Rational and self-adaptive evolutionary extreme learning machine for electricity price forecast

Xiao, C., Dong, Z. Y., Xu, Y. H., Meng, K., Zhou, X. S., & Zhang, X. (2016) · Memetic Computing · doi:10.1007/s12293-016-0195-0 · Cited by 33

Nonlinear Shape-Manifold Learning Approach: Concepts, Tools and Applications

Meng, L., Breitkopf, P., Quilliec, G. L., Raghavan, B., & Villon, P. (2016) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-016-9189-9 · Cited by 34

Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

Alzubaidi, A., Cosma, G., Brown, D. J., & Pockley, A. G. (2016) · International Conference on Interactive Technologies and Games (ITAG) · doi:10.1109/itag.2016.18 · Cited by 33

Polarization-independent broadband beam combining grating with over 98% measured diffraction efficiency from 1023 to 1080 nm

Chen, J., Zhang, Y.-B., Wang, Y., Kong, F., Huang, H., Wang, Y., Jin, Y., Chen, P., Xu, J., & Shao, J. (2017) · Optics Letters · doi:10.1364/ol.42.004016 · Cited by 33

DNA barcoding authentication for the wood of eight endangered Dalbergia timber species using machine learning approaches

He, T., Jiao, L., Yu, M., Guo, J., Jiang, X., & Yin, Y. (2018) · Holzforschung · doi:10.1515/hf-2018-0076 · Cited by 33

A New Optimization Approach for the Least-Cost Design of Water Distribution Networks: Improved Crow Search Algorithm

Fallah, H., Kişi, Ö., Kim, S., & Rezaie-Balf, M. (2019) · Water Resources Management · doi:10.1007/s11269-019-02322-8 · Cited by 33

A long-term fleet renewal problem under uncertainty: A simulation-based optimization approach

Turan, H. H., Elsawah, S., & Ryan, M. J. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113158 · Cited by 33

A better understanding on traffic light scheduling: New cellular GAs and new in-depth analysis of solutions

Villagra, A., Alba, E., & Luque, G. (2020) · Journal of Computational Science · doi:10.1016/j.jocs.2020.101085 · Cited by 33

A semi-custom design methodology and environment for implementing superconductor adiabatic quantum-flux-parametron microprocessors

Ayala, C. L., Saito, R., Tanaka, T., Chen, O., Takeuchi, N., He, Y., & Yoshikawa, N. (2020) · Superconductor Science and Technology · doi:10.1088/1361-6668/ab7ec3 · Cited by 33

Optimizing Water Age and Pressure in Drinking Water Distribution Networks

Kourbasis, N., Patelis, M., Tsitsifli, S., & Kanakoudis, V. (2020) · EWaS International Conference (EWaS) · doi:10.3390/environsciproc2020002051 · Cited by 33

IGA-based point cloud fitting using B-spline surfaces for reverse engineering

Zhao, X., Zhang, C., Xu, L., Yang, B., & Feng, Z. (2013) · Information Sciences · doi:10.1016/j.ins.2013.04.022 · Cited by 33

A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving

Onieva, E., Hernández‐Jayo, U., Osaba, E., Perallos, A., & Zhang, X. (2015) · Information Sciences · doi:10.1016/j.ins.2015.05.036 · Cited by 33

A hybrid correlation-based deep learning model for email spam classification using fuzzy inference system

Ayo, F. E., Ogundele, L. A., Olakunle, S., Awotunde, J. B., & Kasali, F. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100390 · Cited by 32

Parameters optimum matching of pure electric vehicle dual-mode coupling drive system

Zhang, L., Li, L., Qi, B., Wang, C., Zhang, Y., & Song, J. (2014) · Science China Technological Sciences · doi:10.1007/s11431-014-5651-9 · Cited by 32

Multi-layer hybrid machine learning techniques for anomalies detection and classification approach

Aziz, A. S. A., Hassanien, A. E., Hanafi, S. E.-O., & Tolba, M. F. (2013) · International Conference on Hybrid Intelligent Systems (HIS) · doi:10.1109/his.2013.6920485 · Cited by 32

Recent Developments in Derivative-Free Multiobjective Optimisation

Custódio, A. L., Emmerich, M., & Madeira, J. F. A. (2012) · Computational Technology Reviews · doi:10.4203/ctr.5.1 · Cited by 32

Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook

Huettmann, F., Craig, E. H., Herrick, K. A., Baltensperger, A. P., Humphries, G. R. W., Lieske, D. J., Miller, K. B., Mullet, T. C., Oppel, S., Reséndiz‐Infante, C., et al. (2018) · Springer eBooks · doi:10.1007/978-3-319-96978-7_2 · Cited by 33

Soft Computing for Swarm Robotics: New Trends and Applications

Osaba, E., Ser, J. D., Iglesias, A., & Yang, X. (2019) · Journal of Computational Science · doi:10.1016/j.jocs.2019.101049 · Cited by 32

People's Adaptive Side-by-Side Model Evolved to Accompany Groups of People by Social Robots

Repiso, E., Garrell, A., & Sanfeliu, A. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2970676 · Cited by 32

A multi-skilled workforce optimisation in maintenance logistics networks by multi-thread simulated annealing algorithms

Turan, H. H., Kosanoglu, F., & Atmis, M. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1735665 · Cited by 32

Predictive Adaptive Filter for Reducing Total Harmonics Distortion in PV Systems

Alhafadhi, L., Teh, J., Lai, C., & Salem, M. (2020) · Energies · doi:10.3390/en13123286 · Cited by 32

Lévy-flight moth-flame optimisation algorithm-based micro-grid equipment sizing: An integrated investment and operational planning approach

Mohseni, S., Brent, A. C., Burmester, D., & Browne, W. N. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100047 · Cited by 32

Gravity inversion for heterogeneous sedimentary basin with b-spline polynomial approximation using differential evolution algorithm

Roy, A., Dubey, C. P., & Prasad, M. H. (2021) · Geophysics · doi:10.1190/geo2019-0779.1 · Cited by 32

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

Guerreiro, M., Guerreiro, E. M. A., Barchi, T. M., Biluca, J., Alves, T. A., Tadano, Y. D. S., Trojan, F., & Siqueira, H. V. (2021) · Applied Sciences · doi:10.3390/app11219868 · Cited by 35

Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models

Roy, D. K., Biswas, S., Mattar, M. A., El-Shafei, A. A., Murad, K. F. I., Saha, K. K., Datta, B., & Dewidar, A. Z. (2021) · Water · doi:10.3390/w13213130 · Cited by 32

Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization

Zhang, Y., & Chi, A. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01872-2 · Cited by 32

WPO: A Whale Particle Optimization Algorithm

Huang, K.-W., Wu, Z.-X., Jiang, C.-L., Huang, Z.-H., & Lee, S. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00295-6 · Cited by 32

Using syntactic information for improvingwhy-question answering

Verberne, S., Boves, L., Oostdijk, N. H. J., & Coppen, P. A. J. M. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599201 · Cited by 32

Improved optimization technique using hybrid ACO-PSO

Pal, D., Verma, P., Gautam, D., & Indait, P. (2016) · International Conference on Next Generation Computing Technologies · doi:10.1109/ngct.2016.7877428 · Cited by 32

Assessing strategies for protecting transportation infrastructure from an uncertain climate future

Asadabadi, A., & Miller-Hooks, E. D. (2017) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2017.08.010 · Cited by 32

The Random Forest Based Detection of Shadowsock's Traffic

Deng, Z., Liu, Z., Chen, Z., & Guo, Y. (2017) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2017.132 · Cited by 32

Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Sedighi, M., & Roodposhti, F. R. (2022) · Australasian Accounting Business and Finance Journal · doi:10.14453/aabfj.v16i6.05 · Cited by 28

STPGA: Selection of training populations with a genetic algorithmPreprint

Akdemir, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/111989 · Cited by 30

Systemic evolutionary chemical space exploration for drug discovery

Lu, C., Liu, S., Shi, W., Yu, J., Zhou, Z., Zhang, X., Lu, X., Cai, F., Xia, N., & Wang, Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00598-4 · Cited by 30

On the Performance Assessment of ANN and Spotted Hyena Optimized ANN to Predict the Spontaneous Combustion Liability of Coal

Lawal, A. I., Onifade, M., Abdulsalam, J., Aladejare, A. E., Gbadamosi, A. R., & Said, K. O. (2020) · Combustion Science and Technology · doi:10.1080/00102202.2020.1815196 · Cited by 31

Prediction of the axial compression capacity of stub CFST columns using machine learning techniques

Megahed, K., Mahmoud, N. S., & Abd-Rabou, S. E. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-53352-1 · Cited by 34

Intelligent laser-based metal additive manufacturing: A review on machine learning for process optimization and property prediction

Moradi, A., Tajalli, S., Mosallanejad, M. H., & Saboori, A. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14858-0 · Cited by 31

Comparing the performance of multispectral vegetation indices and machine-learning algorithms for remote estimation of chlorophyll content: a case study in the Sundarbans mangrove forest

Gholizadeh, H., Robeson, S. M., & Rahman, A. F. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2015.1054959 · Cited by 31

Discovery of neuroprotective compounds by machine learning approaches

Fang, J., Pang, X., Yan, R., Lian, W., Li, C., Wang, Q., Liu, A.-L., & Du, G.-H. (2016) · RSC Advances · doi:10.1039/c5ra23035g · Cited by 31

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

Lobos, G. A., Escobar-Opazo, A., Estrada, F., Romero‐Bravo, S., Garriga, M., del Pozo, A., Poblete-Echeverría, C., González-Tálice, J., González-Martínez, L., & Caligari, P. D. S. (2019) · Remote Sensing · doi:10.3390/rs11030329 · Cited by 31

Symbolic regression-based improved method for wind speed extrapolation from lower to higher altitudes for wind energy applications

Valsaraj, P., Thumba, D. A., Asokan, K., & Kumar, K. S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114270 · Cited by 31

An Adversarial Approach to Private Flocking in Mobile Robot Teams

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