Genetic Algorithms and Machine Learning
Open accessThe 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
Cites
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?
Gradient-free methods such as Particle Swarm optimization, Genetic Algorithms and Simulated Annealing are more computationally demanding.
“Gradient-free methods such as the Particle Swarm optimization168, Genetic Algorithms169 and Simulated Annealing170 algorithms are more demanding from a computational point of view”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [169]
The citation is attached to Genetic Algorithms, so this verdict concerns that method rather than Particle Swarm Optimization or Simulated Annealing. The sections “Genetic algorithms and classifier systems” and “Arguments for the evolutionary metaphor” describe genetic algorithms as parallel and relatively efficient, but do not compare their computational demands with other optimization methods. The claim of greater computational demand cannot be verified from our copy; page numbers are unavailable. (LLM, conf=0.74)
Experimental — accuracy not yet measuredGradient-free methods are rarely employed for ANN training.
“and hence, they are rarely employed for ANN training”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [169]
The sections “Metaphors for learning” and “Genetic algorithms and classifier systems” discuss neural networks and genetic algorithms, but do not say how often gradient-free methods are used to train artificial neural networks. Searches for ANN training, gradients, and backpropagation found no passage addressing that frequency claim; page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,169 · see citing works
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 · see citing works
Self-citationGenetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391 · see citing works
Self-citationFoxman, D., & Bateson, G. (1973) · The Western Political Quarterly · doi:10.2307/446833 · Cited by 7,253 · see citing works
Bateson, G. (2000) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226924601.001.0001 · Cited by 5,545 · see citing works
Seel, P. D. N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5794 · Cited by 2,566 · see citing works
(1987) · Medical Entomology and Zoology · Cited by 2,512 · see citing works
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 · see citing works
Self-citationHolland, 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 · see citing works
Self-citationOutline for a Logical Theory of Adaptive Systems
Holland, J. H. (1962) · Journal of the ACM · doi:10.1145/321127.321128 · Cited by 1,117 · see citing works
Self-citationProceedings of the First International Conference on Genetic Algorithms and their Applications
Grefenstette, J. J. (2014) · Psychology Press eBooks · doi:10.4324/9781315799674 · Cited by 368 · see citing works
Genetic Algorithms and their Applications
(2013) · Psychology Press eBooks · doi:10.4324/9780203761595 · Cited by 276 · see citing works
Compaction of Symbolic Layout Using Genetic Algorithms
Fourman, M. P. (1985) · international conference on Genetic algorithms · Cited by 192 · see citing works
Genetic algorithms and their applications
Grefenstette, J. J. (1987) · Cited by 130 · see citing works
Genetic algorithms and communication link speed design: theoretical considerations
Davis, L. D., & Coombs, S. (1987) · international conference on Genetic algorithms · Cited by 50 · see citing works
Flagged first, then by citations
1 of the 16 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [7]7. Grefenstette, J. J. (Ed.). (1987). Genetic Algorithms and Their Applica- tions: Proceedings of the Second International Conference on Genetic Al- gorithms. Cambridge, MA: Lawrence Erlbaum.
- [12]12. Waddington, C. H. (1967). Summary discussion. In P. S. Moorhead & M. M Kaplan (Eds.), Mathematical challenges to the neo-Darwinian interpreta- tion of evolution. Philadelphia, PA: Wistar Institute Press.
Cited by
Sorted by citations
Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.07.002 · Cited by 5,096 · see citing works
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332 · see citing works
Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.039 · Cited by 1,859 · see citing works
Mirjalili, S. (2019) · Studies in computational intelligence · doi:10.1007/978-3-319-93025-1_4 · Cited by 1,737 · see citing works
Butterfly optimization algorithm: a novel approach for global optimization
Arora, S., & Singh, S. (2018) · Soft Computing · doi:10.1007/s00500-018-3102-4 · Cited by 1,727 · see citing works
EXPERT SYSTEMS WITH APPLICATIONS
Communication, S., Been-chian Chien, A., Jung Yi Lin, A., Tzung-pei Hong, B., & Com/locate/eswa, W. E. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.10.010 · Cited by 1,670 · see citing works
Dorri, A., Kanhere, S. S., & Jurdak, R. (2018) · IEEE Access · doi:10.1109/access.2018.2831228 · Cited by 1,049 · see citing works
A survey on new generation metaheuristic algorithms
Dökeroğlu, T., Sevinç, E., Küçükyılmaz, T., & Coşar, A. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.106040 · Cited by 935 · see citing works
Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications
Trojovský, P., & Dehghani, M. (2022) · Sensors · doi:10.3390/s22030855 · Cited by 901 · see citing works
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Lv, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2983860 · Cited by 873 · see citing works
Genetic algorithms as a strategy for feature selection
Leardi, R., Boggia, R., & Terrile, M. (1992) · Journal of Chemometrics · doi:10.1002/cem.1180060506 · Cited by 831 · see citing works
Machine learning for digital soil mapping: Applications, challenges and suggested solutions
Wadoux, A. M. J. ‐., Minasny, B., & McBratney, A. B. (2020) · Earth-Science Reviews · doi:10.1016/j.earscirev.2020.103359 · Cited by 752 · see citing works
Genetic algorithms: theory, genetic operators, solutions, and applications
Alhijawi, B., & Awajan, A. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00822-6 · Cited by 739 · see citing works
A survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha, T., Tung, T. M., & Yaseen, Z. M. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124670 · Cited by 656 · see citing works
Metaheuristic design of feedforward neural networks: A review of two decades of research
Ojha, V., Abraham, A., & Snåšel, V. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.01.013 · Cited by 613 · see citing works
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems
Dehghani, M., Hubálovský, Š., & Trojovský, P. (2021) · IEEE Access · doi:10.1109/access.2021.3133286 · Cited by 580 · see citing works
Dehghani, M., & Trojovský, P. (2023) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2022.1126450 · Cited by 572 · see citing works
Deep learning in the construction industry: A review of present status and future innovations
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A., Delgado, J. M. D., Akinadé, O. O., & Ahmed, A. A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101827 · Cited by 544 · see citing works
Trojovská, E., Dehghani, M., & Trojovský, P. (2022) · IEEE Access · doi:10.1109/access.2022.3172789 · Cited by 539 · see citing works
Colloquium : Criticality and dynamical scaling in living systems
Muñoz, M. A. (2018) · Reviews of Modern Physics · doi:10.1103/revmodphys.90.031001 · Cited by 527 · see citing works
A novel genetic LSTM model for wind power forecast
Shahid, F., Zameer, A., & Muneeb, M. (2021) · Energy · doi:10.1016/j.energy.2021.120069 · Cited by 533 · see citing works
Sowmya, R., Premkumar, M., & Jangir, P. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107532 · Cited by 530 · see citing works
Abdmouleh, Z., Gastli, A., Ben‐Brahim, L., Haouari, M., & Al-Emadi, N. A. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.05.087 · Cited by 508 · see citing works
Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems
Braik, M. S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114685 · Cited by 508 · see citing works
Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 499 · see citing works
Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · Nature · doi:10.1038/s41586-023-06924-6 · Cited by 544 · see citing works
Golden eagle optimizer: A nature-inspired metaheuristic algorithm
Mohammadi-Balani, A., Nayeri, M. D., Azar, A., & Taghizadeh, M. R. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.107050 · Cited by 484 · see citing works
Opposition based learning: A literature review
Mahdavi, S., Rahnamayan, S., & Deb, K. (2017) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2017.09.010 · Cited by 477 · see citing works
Metaheuristics in large-scale global continues optimization: A survey
Mahdavi, S., Shiri, M. E., & Rahnamayan, S. (2014) · Information Sciences · doi:10.1016/j.ins.2014.10.042 · Cited by 465 · see citing works
Big Data in Natural Disaster Management: A Review
Yu, M., Yang, C. P., & Li, Y. (2018) · Geosciences · doi:10.3390/geosciences8050165 · Cited by 454 · see citing works
Genetic algorithm-based heuristic for feature selection in credit risk assessment
Oreški, S., & Oreški, G. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.09.004 · Cited by 430 · see citing works
Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103330 · Cited by 426 · see citing works
The Impact of Automated Parameter Optimization on Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A., & Matsumoto, K.-I. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2794977 · Cited by 406 · see citing works
MiarNaeimi, F., Azizyan, G., & Rashki, M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106711 · Cited by 400 · see citing works
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
Tama, B. A., Comuzzi, M., & Rhee, K.-H. (2019) · IEEE Access · doi:10.1109/access.2019.2928048 · Cited by 387 · see citing works
Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
Zhang, X., Xie, F., Ji, T., Zhu, Z., & Zheng, Y. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113485 · Cited by 382 · see citing works
Kůdela, J., & Matoušek, R. (2022) · Soft Computing · doi:10.1007/s00500-022-07362-8 · Cited by 379 · see citing works
Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model
Shakoor, R., Hassan, M. Y., Raheem, A., & Wu, Y. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.229 · Cited by 378 · see citing works
A Review of Android Malware Detection Approaches Based on Machine Learning
Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., & Liu, H. (2020) · IEEE Access · doi:10.1109/access.2020.3006143 · Cited by 396 · see citing works
Tackling environmental challenges in pollution controls using artificial intelligence: A review
Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., & Wang, J. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134279 · Cited by 375 · see citing works
Coronavirus herd immunity optimizer (CHIO)
Al‐Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Doush, I. A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05296-6 · Cited by 375 · see citing works
Improved salp swarm algorithm based on particle swarm optimization for feature selection
Ibrahim, R. A., Ewees, A. A., Oliva, D. A., Elaziz, M. A., & Lu, S. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1031-9 · Cited by 374 · see citing works
Qian, Y.-J., Guo, X., Zhang, Y.-L., Peng, Y., Sun, P., Huang, C.-H., Niu, J., Zhou, X., & Crittenden, J. (2016) · Environmental Science and Technology · doi:10.1021/acs.est.5b03715 · Cited by 334 · see citing works
Optimization of problems with multiple objectives using the multi-verse optimization algorithm
Mirjalili, S., Jangir, P., Mirjalili, S. Z., Saremi, S., & Trivedi, I. N. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.07.018 · Cited by 371 · see citing works
Mangalathu, S., & Jeon, J. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.01.008 · Cited by 366 · see citing works
A comprehensive survey on recent metaheuristics for feature selection
Dökeroğlu, T., Deniz, A., & Kızılöz, H. E. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.083 · Cited by 361 · see citing works
Bui, D. T., Pradhan, B., Nampak, H., Bui, Q., Tran, Q.-A., & Nguyen, Q.-P. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.06.027 · Cited by 356 · see citing works
Seyedmahmoudian, M., Horan, B., Tey, K. S., Rahmani, R., Oo, A. M. T., Mekhilef, S., & Stojcevski, A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.06.053 · Cited by 356 · see citing works
Chaotic grasshopper optimization algorithm for global optimization
Arora, S., & Anand, P. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3343-2 · Cited by 351 · see citing works
Completely Automated CNN Architecture Design Based on Blocks
Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2919608 · Cited by 341 · see citing works
A new meta-heuristic optimizer: Pathfinder algorithm
Yapıcı, H., & Çetinkaya, N. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.012 · Cited by 340 · see citing works
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
Yao, K., Unni, R., & Zheng, Y. (2018) · Nanophotonics · doi:10.1515/nanoph-2018-0183 · Cited by 323 · see citing works
Souza, P. V. D. C. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106275 · Cited by 312 · see citing works
Trojovský, P., & Dehghani, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-35863-5 · Cited by 308 · see citing works
A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem
Sharma, M., & Kaur, P. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09412-6 · Cited by 304 · see citing works
Wong, L. A., Ramachandaramurthy, V. K., Taylor, P., Ekanayake, J. B., Walker, S. L., & Padmanaban, S. (2018) · Journal of Energy Storage · doi:10.1016/j.est.2018.12.015 · Cited by 309 · see citing works
Genetic Algorithm Based on Natural Selection Theory for Optimization Problems
Albadr, M. A. A., Tiun, S. B., Ayob, M. B., & AL‐Dhief, F. T. (2020) · Symmetry · doi:10.3390/sym12111758 · Cited by 312 · see citing works
Wani, J. A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., & Singh, S. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09588-5 · Cited by 320 · see citing works
Development of soft computing and applications in agricultural and biological engineering
Huang, Y., Lan, Y., Thomson, S. J., Fang, A., Hoffmann, W. C., & Lacey, R. E. (2010) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2010.01.001 · Cited by 303 · see citing works
Understanding and using genetic algorithms Part 1. Concepts, properties and context
Lucasius, C. B., & Kateman, G. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)80079-w · Cited by 296 · see citing works
Xing, W.-L., Guo, R., Petakovic, E., & Goggins, S. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.034 · Cited by 306 · see citing works
HYDRA: Massively Compositional Model for Cross-Project Defect Prediction
Xia, X., Lo, D., Pan, S. J., Nagappan, N., & Wang, X. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2543218 · Cited by 293 · see citing works
Dehghani, M., Hubálovský, Š., & Trojovský, P. (2022) · IEEE Access · doi:10.1109/access.2022.3151641 · Cited by 284 · see citing works
Methods for multi-objective optimization: An analysis
Giagkiozis, I., & Fleming, P. J. (2014) · Information Sciences · doi:10.1016/j.ins.2014.08.071 · Cited by 275 · see citing works
Trojovský, P., & Dehghani, M. (2023) · Biomimetics · doi:10.3390/biomimetics8020149 · Cited by 275 · see citing works
Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems
Jordehi, A. R. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.10.026 · Cited by 264 · see citing works
Shadmand, M. B., & Balog, R. S. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2315043 · Cited by 261 · see citing works
Raabe, D., Sun, B., da Silva, A. K., Gault, B., Yen, H., Sedighiani, K., Sukumar, P. T., Filho, I. R. D. S., Katnagallu, S., Jägle, E. A., et al. (2020) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-020-05947-2 · Cited by 261 · see citing works
A knowledge-intensive genetic algorithm for supervised learning
Janikow, C. Z. (1994) · Machine Learning · doi:10.1007/bf00993043 · Cited by 248 · see citing works
Rodrigues, T. K., Suto, K., Nishiyama, H., Liu, J., & Kato, N. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2943405 · Cited by 246 · see citing works
Abualigah, L., Elaziz, M. A., Khasawneh, A. M., Alshinwan, M., Ibrahim, R. A., Al‐qaness, M. A. A., Mirjalili, S., Sumari, P., & Gandomi, A. H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06747-4 · Cited by 250 · see citing works
Zhu, F., Li, G., Tang, H., Li, Y., Lv, X., & Wang, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121219 · Cited by 244 · see citing works
Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing
Aburukba, R., Alikarar, M. A. N., Landolsi, T., & El‐Fakih, K. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.09.039 · Cited by 243 · see citing works
Arora, S., Singh, H., Sharma, M., Sharma, S., & Anand, P. (2019) · IEEE Access · doi:10.1109/access.2019.2897325 · Cited by 241 · see citing works
Genetic Algorithms and Their Applications
Gen, M., & Lin, L. (2023) · Springer handbooks · doi:10.1007/978-1-4471-7503-2_33 · Cited by 236 · see citing works
Nakisa, B., Rastgoo, M., Tjondronegoro, D., & Chandran, V. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2017.09.062 · Cited by 222 · see citing works
A machine learning model for emotion recognition from physiological signals
Domínguez-Jiménez, J. A., Campo-Landines, K. C., Martínez-Santos, J. C., Delahoz, E. J., & Contreras-Ortiz, S. H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101646 · Cited by 232 · see citing works
Sharif, S. A., & Hammad, A. (2018) · Journal of Building Engineering · doi:10.1016/j.jobe.2018.11.006 · Cited by 232 · see citing works
Information and Communication Technology Solutions for the Circular Economy
Demestichas, K. P., & Daskalakis, E. (2020) · Sustainability · doi:10.3390/su12187272 · Cited by 257 · see citing works
Prostate cancer detection using deep convolutional neural networks
Yoo, S., Gujrathi, I., Haider, M. A., & Khalvati, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-55972-4 · Cited by 230 · see citing works
Dehghani, M., Trojovská, E., & Trojovský, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-14225-7 · Cited by 229 · see citing works
Yang, Z., Li, K., & Foley, A. M. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.06.007 · Cited by 228 · see citing works
Cooner, A. J., Shao, Y., & Campbell, J. B. (2016) · Remote Sensing · doi:10.3390/rs8100868 · Cited by 223 · see citing works
The determinants of crowdfunding success: A semantic text analytics approach
Hui, Y., Lau, R. Y. K., & Xu, W. X. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.08.001 · Cited by 222 · see citing works
eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks
Clarke, D. J. B., Kuleshov, M. V., Schilder, B. M., Torre, D., Duffy, M. E., Keenan, A. B., Lachmann, A., Feldmann, A. S., Gundersen, G. W., Silverstein, M. C., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky458 · Cited by 217 · see citing works
What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation
Forrest, S., & Mitchell, M. (1994) · Machine Learning · doi:10.1007/bf00993046 · Cited by 217 · see citing works
Topology Optimization of Photonic and Phononic Crystals and Metamaterials: A Review
Li, W., Meng, F., Chen, Y., Li, Y., & Huang, X. (2019) · Advanced Theory and Simulations · doi:10.1002/adts.201900017 · Cited by 213 · see citing works
Short-term load forecasting in a non-residential building contrasting models and attributes
Massana, J., Pous, C., Burgas, L., Meléndez, J., & Colomer, J. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.02.007 · Cited by 212 · see citing works
What Security Questions Do Developers Ask? A Large-Scale Study of Stack Overflow Posts
Yang, X.-L., Lo, D., Xia, X., Wan, Z.-Y., & Sun, J.-L. (2016) · Journal of Computer Science and Technology · doi:10.1007/s11390-016-1672-0 · Cited by 209 · see citing works
Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction
Mirjalili, S., Dong, J. S., Sadiq, A. S., & Faris, H. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-12127-3_5 · Cited by 210 · see citing works
Advanced metaheuristic optimization techniques in applications of deep neural networks: a review
Elaziz, M. A., Dahou, A., Abualigah, L., Yu, L., Alshinwan, M., Khasawneh, A. M., & Lu, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05960-5 · Cited by 219 · see citing works
Yuan, Z., Wang, L., & Ji, X. (2013) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.09.004 · Cited by 208 · see citing works
Adoption of Machine Learning Techniques in Ecology and Earth Science
Thessen, A. (2016) · One Ecosystem · doi:10.3897/oneeco.1.e8621 · Cited by 207 · see citing works
Leardi, R. (1994) · Journal of Chemometrics · doi:10.1002/cem.1180080107 · Cited by 202 · see citing works
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks
Qi, F., Chen, Y., Li, M., Yao, Y., Liu, Z., & Sun, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.752 · Cited by 222 · see citing works
GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network
Sahoo, B. M., Pandey, H. M., & Amgoth, T. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100772 · Cited by 200 · see citing works
Histogram Equalization Variants as Optimization Problems: A Review
Dhal, K. G., Das, A., Ray, S., Gálvez, J., & Das, S. K. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09425-1 · Cited by 199 · see citing works
Taherkhani, N., & Pierre, S. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2546555 · Cited by 198 · see citing works
Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming
Salgotra, R., Gandomi, M., & Gandomi, A. H. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109945 · Cited by 198 · see citing works
Talaat, M., Elkholy, M. H., Alblawi, A., & Said, T. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10410-w · Cited by 215 · see citing works
- Authors
- Goldberg, D. E., & Holland, J. H.
- Affiliations
- University of Alabama (US)
- University of Michigan (US)
- Venue
- Machine Learning
- Published
- 1988-10
- Volume
- 3
- Issue
- 2-3
- Pages
- 95-99
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- 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
- 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
