Cited by 3,193Evolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchNeural Networks and Applications

The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.

Genetic Algorithms and Machine Learning

Open access

Goldberg, David E., Holland, John H.·1988·Machine Learning

DOI 10.1023/a:1022602019183

Open this paper in CoChat — free account Open PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1023/a:1022602019183

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Self-citation
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Self-citation
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
Self-citation
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
Self-citation
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
Self-citation

Flagged first, then by citations

Show all 16 references

1 of the 16 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Waddington, C. H. (1967). Summary discussion. In P. S. Moorhead & M. M Kaplan (Eds.), Mathematical challenges to the neo-Darwinian interpretation of evolution. Philadelphia, PA: Wistar Institute Press.

Sorted by citations

Items 301–400 of 1,007

A new optimization algorithm based on mimicking the voting process for leader selection
Trojovský, P., & Dehghani, M. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.976 · Cited by 82
An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups
Li, Z., Qian, B., Hu, R., Chang, L., & Yang, J. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.027 · Cited by 81
Compensation for temperature-dependent phase and velocity of guided wave signals in baseline subtraction for structural health monitoring
Mariani, S., Heinlein, S., & Cawley, P. (2019) · Structural Health Monitoring · doi:10.1177/1475921719835155 · Cited by 81
Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection
Haq, A. U., Zhang, D., Peng, H., & Rahman, S. U. (2019) · IEEE Access · doi:10.1109/access.2019.2947701 · Cited by 87
Uncertain wind power forecasting using LSTM‐based prediction interval
Banik, A., Behera, C., Sarathkumar, T. V., & Goswami, A. (2020) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2019.1238 · Cited by 69
A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems
Zhang, Q., & Lixin, D. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.005 · Cited by 79
Memetic Harris Hawks Optimization: Developments and perspectives on project scheduling and QoS-aware web service composition
Li, C., Li, J., Chen, H., & Heidari, A. A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114529 · Cited by 80
Hybrid genetic algorithm-based optimization of powertrain and control parameters of plug-in hybrid electric bus
Li, L., Zhang, Y., Yang, C., Jiao, X., Zhang, L., & Song, J. (2014) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2014.10.016 · Cited by 80
Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons
Teixeira, A. L., Leal, J. P., & Falcão, A. O. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-9 · Cited by 81
A two-stage differential biogeography-based optimization algorithm and its performance analysis
Zhao, F., Qin, S., Zhang, Y., Ma, W., Zhang, C., & Song, H. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.012 · Cited by 79
Enhanced a hybrid moth-flame optimization algorithm using new selection schemes
Shehab, M., Alshawabkah, H., Abualigah, L., & Al-Madi, N. A. (2020) · Engineering With Computers · doi:10.1007/s00366-020-00971-7 · Cited by 79
Short-Term PV Power Prediction Based on Optimized VMD and LSTM
Wang, L., Liu, Y., Li, T., Xie, X., & Chang, C.-M. (2020) · IEEE Access · doi:10.1109/access.2020.3022246 · Cited by 82
A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit
Shalaby, A., Elkamel, A., Douglas, P. L., Zhu, Q., & Zheng, Q. P. (2020) · Energy · doi:10.1016/j.energy.2020.119113 · Cited by 79
Development and validation of a versatile hysteretic model for pre-compressed self-centering buckling-restrained brace
Xu, L., Chen, P., & Li, Z. (2020) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2020.106473 · Cited by 79
Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC’s in power systems
Jordehi, A. R., Jasni, J., Wahab, N. I. A., Kadir, M. Z. A. A., & Javadi, M. S. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.07.058 · Cited by 78
Advances in reduction of total harmonic distortion in solar photovoltaic systems: A literature review
Alhafadhi, L., & Teh, J. (2019) · International Journal of Energy Research · doi:10.1002/er.5075 · Cited by 78
Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
Alsayyed, O., Hamadneh, T., Al-Tarawneh, H., Alqudah, M., Gochhait, S., Leonova, I. S., Malik, O. P., & Dehghani, M. (2023) · Biomimetics · doi:10.3390/biomimetics8080619 · Cited by 78
UAV path planning techniques: a survey
Ghambari, S., Golabi, M., Jourdan, L., Lepagnot, J., & Idoumghar, L. (2024) · RAIRO - Operations Research · doi:10.1051/ro/2024073 · Cited by 84
Time series wind power forecasting based on variant Gaussian Process and TLBO
Yan, J., Li, K., Bai, E., Yang, Z., & Foley, A. M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.081 · Cited by 78
Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm
Ren, Q., Li, M., Zhang, M., Shen, Y., & Si, W. (2019) · Applied Sciences · doi:10.3390/app9142802 · Cited by 80
An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering
da Silva, E. Q., Camilo-Junior, C. G., Pascoal, L. M. L., & Rosa, T. C. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.12.050 · Cited by 77
Impact of Molecular Descriptors on Computational Models
Grisoni, F., Consonni, V., & Todeschini, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8639-2_5 · Cited by 77
Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach
Storti, B., Dorella, J. J., Román, N. D., Peralta, I., & Albanesi, A. E. (2019) · Energy · doi:10.1016/j.energy.2019.07.144 · Cited by 77
Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data
Savage, D. J., Feng, Z., & Knežević, M. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113747 · Cited by 77
Data-driven optimized layout of battery electric vehicle charging infrastructure
Tao, Y., Huang, M., & Yang, L. (2018) · Energy · doi:10.1016/j.energy.2018.03.018 · Cited by 76
Elitist non-dominated sorting Harris hawks optimization: Framework and developments for multi-objective problems
Jangir, P., Heidari, A. A., & Chen, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115747 · Cited by 78
Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends
Hanoon, M. S., Ahmed, A. N., Fai, C. M., Birima, A. H., Razzaq, A., Sherif, M. M., Sefelnasr, A., & El‐Shafie, A. (2021) · Water Air & Soil Pollution · doi:10.1007/s11270-021-05311-z · Cited by 80
Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization
Abasi, A. K., Makhadmeh, S. N., Al‐Betar, M. A., Alomari, O. A., Awadallah, M. A., Alyasseri, Z. A. A., Doush, I. A., Elnagar, A., Alkhammash, E. H., & Hadjouni, M. (2022) · Applied Sciences · doi:10.3390/app121910057 · Cited by 76
Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm
Dhavle, S. V., Kulkarni, A. J., Shastri, A., & Kale, I. R. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2683-z · Cited by 76
Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation
Zhou, Y., Yang, X. P., Ling, Y., & Zhang, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5637-x · Cited by 75
Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)
Deb, S., Gao, X., Tammi, K., Kalita, K., & Mahanta, P. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09718-3 · Cited by 76
Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study
Ezugwu, A. E.-S. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-2073-0 · Cited by 75
Optimal caching scheme in D2D networks with multiple robot helpers
Lin, Y. C., Song, H., Ke, F., Yan, W., Liu, Z., & Cai, F. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.09.027 · Cited by 78
Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques
Mazumdar, D., Sain, C., Biswas, P. K., Sanjeevikumar, P., & Khan, B. (2024) · International Transactions on Electrical Energy Systems · doi:10.1155/2024/8363342 · Cited by 88
Using genetic algorithms for robot motion planning
Ahuactzin, J.-M., Talbi, E., Bessìère, P., & Mazer, E. (1993) · Lecture notes in computer science · doi:10.1007/3-540-57132-9_6 · Cited by 75
Control of the reactor core power in PWR using optimized PID controller with the real-coded GA
Mousakazemi, S. M. H., Ayoobian, N., & Ansarifar, G. R. (2018) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2018.03.038 · Cited by 74
Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
Shishvan, O. R., Zois, D., & Soyata, T. (2018) · IEEE Access · doi:10.1109/access.2018.2866049 · Cited by 73
Design of computational intelligent procedure for thermal analysis of porous fin model
Ahmad, I., Zahid, H., Ahmad, F., Raja, M. A. Z., & Bǎleanu, D. (2019) · Chinese Journal of Physics · doi:10.1016/j.cjph.2019.04.015 · Cited by 74
Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging
Gao, B., Li, X., Woo, W. L., & Tian, G. Y. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2783627 · Cited by 74
A metamodel-based multi-objective optimization method to balance thermal comfort and energy efficiency in a campus gymnasium
Yue, N., Li, L., Morandi, A., & Zhao, Y. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111513 · Cited by 77
A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles
Panigrahy, S. K., & Emany, H. (2023) · Sensors · doi:10.3390/s23010555 · Cited by 78
Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People
Chernbumroong, S., Cang, S., & Yu, H. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2313473 · Cited by 73
Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation
Derivaux, S., Forestier, G., Wemmert, C., & Lefèvre, S. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.07.007 · Cited by 73
Survey on artificial intelligence for additive manufacturing
Yang, J., Chen, Y., Huang, W., & Li, Y. (2017) · 2017 23rd International Conference on Automation and Computing (ICAC) · doi:10.23919/iconac.2017.8082053 · Cited by 73
Genetic algorithm enhanced by machine learning in dynamic aperture optimization
Li, Y., Cheng, W., Yu, L. H., & Rainer, R. (2018) · Physical Review Accelerators and Beams · doi:10.1103/physrevaccelbeams.21.054601 · Cited by 73
A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market
Deng, S., Wang, C., Wang, M., & Sun, Z. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105652 · Cited by 72
A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization
Dhiman, G., Soni, M., Pandey, H. M., Słowik, A., & Kaur, H. (2020) · Engineering With Computers · doi:10.1007/s00366-020-00986-0 · Cited by 72
Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network
Mishra, D., Naik, B., Nayak, J., Souri, A., Dash, P. B., & Vimal, S. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.10.004 · Cited by 72
Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review
Rogers, M., Blanc-Talon, J., Urschler, M., & Delmas, P. J. (2023) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-023-02044-x · Cited by 83
Cell formation in a cellular manufacturing system using simulation integrated hybrid genetic algorithm
Imran, M., Kang, C., Lee, Y. H., Jahanzaib, M., & Aziz, M. H. (2016) · Computers & Industrial Engineering · doi:10.1016/j.cie.2016.12.028 · Cited by 72
A hybrid gene selection method for microarray recognition
Shukla, A. K., Singh, P., & Vardhan, M. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2018.08.004 · Cited by 71
Robust design optimization of laminated plates under uncertain bounded buckling loads
Kaveh, A., Eslamlou, A. D., & Malek, N. G. (2018) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-018-2106-0 · Cited by 71
Improved salp swarm algorithm based on weight factor and adaptive mutation
Wu, J., Nan, R., & Chen, L. (2019) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2019.1572659 · Cited by 71
A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions
Attea, B. A., Abbood, A. D., Hasan, A. A., Pizzuti, C., Al-Ani, M. F., Özdemi̇r, S., & Al-Dabbagh, R. D. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100885 · Cited by 72
Genetic Algorithm-Based Identification of Fractional-Order Systems
Zhou, S., Cao, J., & Chen, Y. (2013) · Entropy · doi:10.3390/e15051624 · Cited by 71
GeFeS: A generalized wrapper feature selection approach for optimizing classification performance
Sahebi, G., Movahedi, P., Ebrahimi, M., Pahikkala, T., Plosila, J., & Tenhunen, H. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103974 · Cited by 70
A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems
Braune, R., Benda, F., Doerner, K. F., & Hartl, R. F. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2021.108342 · Cited by 70
EOSMA: An Equilibrium Optimizer Slime Mould Algorithm for Engineering Design Problems
Yin, S., Luo, Q., & Zhou, Y. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06513-7 · Cited by 75
Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence
Kang, J. R. W., Lee, H. J., Jeong, S., Lee, H. S., & Oh, K. J. (2020) · Sustainability · doi:10.3390/su12072899 · Cited by 74
Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection
Ayo, F. E., Folorunso, S. O., Abayomi-Alli, A. A., Adekunle, A. O., & Awotunde, J. B. (2020) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2020.1767240 · Cited by 69
A new approach for internet traffic classification: GA-WK-ELM
Ertam, F., & Avcı, E. (2016) · Measurement · doi:10.1016/j.measurement.2016.10.001 · Cited by 69
Adaptive Range Genetic Algorithm: A hybrid optimization approach and its application in the design and economic optimization of Shell-and-Tube Heat Exchanger
Iyer, V. H., Mahesh, S., Malpani, R., Sapre, M. S., & Kulkarni, A. J. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.07.001 · Cited by 70
Underwater Target Tracking in Uncertain Multipath Ocean Environments
Liu, B., Tang, X., Tharmarasa, R., Kirubarajan, T., Jassemi-Zargani, R., & Hallé, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.3003703 · Cited by 68
Optimal design of tuned liquid column damper inerter for vibration control
Matteo, A. D., Masnata, C., Adam, C., & Pirrotta, A. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108553 · Cited by 68
Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling
Liu, Y., Feng, Y., & Pontius, R. G. (2014) · Land · doi:10.3390/land3030719 · Cited by 68
Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks
Banimelhem, O., Mowafi, M. Y., & Aljoby, W. (2013) · Communications and Network · doi:10.4236/cn.2013.54034 · Cited by 68
Spoken language identification based on optimised genetic algorithm–extreme learning machine approach
Albadr, M. A. A., Tiun, S. B., Ayob, M. B., & AL‐Dhief, F. T. (2019) · International Journal of Speech Technology · doi:10.1007/s10772-019-09621-w · Cited by 67
Solving energy management of renewable integrated microgrid systems using crow search algorithm
Dey, B., Bhattacharyya, B., Srivastava, A., & Shivam, K. (2019) · Soft Computing · doi:10.1007/s00500-019-04553-8 · Cited by 67
Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations
Pereira, L. D. L., Yahyaoui, I., Fiorotti, R., de Menezes, L. S., Fardin, J. F., Rocha, H. R. O., & Tadeo, F. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118097 · Cited by 67
Green Anaconda Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
Dehghani, M., Trojovský, P., & Malik, O. P. (2023) · Biomimetics · doi:10.3390/biomimetics8010121 · Cited by 67
Enhancing power transfer capability through flexible AC transmission system devices: a review
Albatsh, F. M., Mekhilef, S., Ahmad, S., Mokhlis, H., & Hassan, M. (2015) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1500019 · Cited by 69
Energy-saving thermally coupled ternary extractive distillation process using ionic liquids as entrainer for separating ethyl acetate-ethanol-water ternary mixture
Ma, S., Shang, X., Li, L., Song, Y., Pan, Q., & Sun, L. (2019) · Separation and Purification Technology · doi:10.1016/j.seppur.2019.05.103 · Cited by 67
Determination of the optimal sales level of perishable goods in a two-echelon supply chain network
Salehi-Amiri, A., Zahedi, A., Kazemi, M., Soroor, J., & Hajiaghaei–Keshteli, M. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.106156 · Cited by 67
Exploring misclassifications of robust neural networks to enhance adversarial attacks
Schwinn, L., Raab, R., Nguyen, A., Zanca, D., & Eskofier, B. M. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04532-5 · Cited by 77
Hyper-Heuristics to customise metaheuristics for continuous optimisation
Cruz‐Duarte, J. M., Amaya, I., Ortíz-Bayliss, J. C., Conant-Pablos, S. E., Terashima‐Marín, H., & Shi, Y. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100935 · Cited by 66
Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
Hai, T., Al-Khafaji, Z. S., Qi, C., Zounemat‐Kermani, M., Kişi, Ö., Tiyasha, T., Chau, K., Nourani, V., Melesse, A. M., Elhakeem, M., et al. (2021) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2021.1984992 · Cited by 73
Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review
Fernandes, J. M. R. C., Homayouni, S. M., & Fontes, D. B. M. M. (2022) · Sustainability · doi:10.3390/su14106264 · Cited by 66
Assessing the stability of rock slopes with respect to flexural toppling failure using a limit equilibrium model and genetic algorithm
Zheng, Y. R., Chen, C., Meng, F., Liu, T., & Xia, K. (2020) · Computers and Geotechnics · doi:10.1016/j.compgeo.2020.103619 · Cited by 66
3-D Printed All-Dielectric Frequency Selective Surface With Large Bandwidth and Field of View
Barton, J. H., Garcia, C. R., Berry, E. A., Salas, R., & Rumpf, R. C. (2015) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2015.2388541 · Cited by 65
Benchmarking of best practices: an overview of the academic literature
de Castro, V. F., & Frazzon, E. M. (2017) · Benchmarking An International Journal · doi:10.1108/bij-03-2016-0031 · Cited by 70
A self-adaptive harmony PSO search algorithm and its performance analysis
Zhao, F., Liu, Y., Zhang, C., & Wang, J. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.05.035 · Cited by 65
The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks
Chen, M., Hsiao, Y.-H., Reddy, R. H., & Tiwari, M. K. (2016) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2016.04.003 · Cited by 65
In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9
Nembri, S., Grisoni, F., Consonni, V., & Todeschini, R. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17060914 · Cited by 66
Clustering algorithms: A comparative approach
Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. D. F., & Rodrigues, F. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210236 · Cited by 157
Toward Growing Robots: A Historical Evolution from Cellular to Plant-Inspired Robotics
Dottore, E. D., Sadeghi, A., Mondini, A., Mattoli, V., & Mazzolai, B. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00016 · Cited by 64
A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory
Shukla, A. K., Singh, P., & Vardhan, M. (2019) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026819500202 · Cited by 65
A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection
Arora, S., Sharma, M., & Anand, P. (2020) · Applied Artificial Intelligence · doi:10.1080/08839514.2020.1712788 · Cited by 64
Election Algorithm for Random k Satisfiability in the Hopfield Neural Network
Abubakar, H., Sathasivam, S., Kasihmuddin, M. S. M., & Kasihmuddin, M. S. M. (2020) · Processes · doi:10.3390/pr8050568 · Cited by 64
Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm
Rodríguez, A., Del-Valle-Soto, C., & Velázquez, R. (2020) · Mathematics · doi:10.3390/math8091515 · Cited by 64
Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems
Hubálovská, M., Hubálovský, Š., & Trojovský, P. (2024) · Biomimetics · doi:10.3390/biomimetics9030137 · Cited by 64
Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications
Abu-Falahah, I., Al-Baik, O., Alomari, S. A., Bektemyssova, G., Gochhait, S., Leonova, I. S., Malik, O. P., Werner, F. M., & Dehghani, M. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.053189 · Cited by 89
Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications
Lin, L., & Gen, M. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1437288 · Cited by 64
Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation
Mohseni, S., Brent, A. C., Kelly, S., Browne, W. N., & Burmester, D. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116563 · Cited by 64
Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems
Zhang, X., & Wen, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115032 · Cited by 64
Vehicular fog gateways selection on the internet of vehicles: A fuzzy logic with ant colony optimization based approach
Jabri, I., Mekki, T., Rachedi, A., & Jemaa, M. B. (2019) · Ad Hoc Networks · doi:10.1016/j.adhoc.2019.101879 · Cited by 62
Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems
Braik, M. S., Al-Zoubi, H. R., Ryalat, M. H., Sheta, A. F., & Alzubi, O. A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10164-x · Cited by 63
Intelligent smart home energy efficiency model using artificial TensorFlow engine
Jo, H., & Yoon, Y. I. (2018) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-018-0132-y · Cited by 62
Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications
Yuan, M., Paradiso, S. P., Meredig, B., & Niezgoda, S. R. (2018) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-018-0123-x · Cited by 62
De novo design of anticancer peptides by ensemble artificial neural networks
Grisoni, F., Neuhaus, C. S., Hishinuma, M., Gabernet, G., Hiss, J. A., Kotera, M., & Schneider, G. (2019) · Journal of Molecular Modeling · doi:10.1007/s00894-019-4007-6 · Cited by 62
Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM
Yu, X., Liu, J., Keung, J., Li, Q., Bennin, K. E., Xu, Z., Wang, J. P., & Cui, X. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2931559 · Cited by 61

Authors: Goldberg, D. E., & Holland, J. H.
Affiliations: University of Alabama (US)
University of Michigan (US)
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 1988-10
Volume: 3
Issue: 2-3
Pages: 95-99
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1023/a:1022602019183
OpenAlex: W1541288193
Cited by: 3,193
References: 16
Citations by year: 2026: 105 · 2025: 151 · 2024: 160 · 2023: 204 · 2022: 249 · 2021: 342 · 2020: 378 · 2019: 366 · 2018: 265 · 2017: 239 · 2016: 212 · 2015: 145 · 2014: 162 · 2013: 94 · 2012: 29
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Machine Learning (publishedVersion)
Deep Blue (University of Michigan) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: genetic algorithm

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Genetic Algorithms and Machine Learning

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks