The DOI matches its Crossref record.
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
Abstract
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements. Bradford Books imprint
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
Exploration and Exploitation in Organizational Learning
March, J. G. (1991) · Organization Science · doi:10.1287/orsc.2.1.71 · Cited by 21,942
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
A review on genetic algorithm: past, present, and future
Katoch, S., Chauhan, S. S., & Kumar, V. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10139-6 · Cited by 4,655
Mathews, D. H., Sabina, J., Zuker, M., & Turner, D. H. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.2700 · Cited by 3,795
(1998) · Cited by 3,476
Use of proteomic patterns in serum to identify ovarian cancer
Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07746-2 · Cited by 3,236
Genetic Algorithms and Machine Learning
Goldberg, D. E., & Holland, J. H. (1988) · Machine Learning · doi:10.1023/a:1022602019183 · Cited by 3,193
Self-citationReservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
A diagnostic approach for going beyond panaceas
Ostrom, E. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0702288104 · Cited by 2,728
Being there: putting brain, body, and world together again
(1997) · Choice Reviews Online · doi:10.5860/choice.34-6204 · Cited by 2,631
The General Utility Lattice Program ( GULP )
Gale, J. D., & Rohl, A. L. (2003) · Molecular Simulation · doi:10.1080/0892702031000104887 · Cited by 2,265
Adaptation on Rugged Landscapes
Levinthal, D. A. (1997) · Management Science · doi:10.1287/mnsc.43.7.934 · Cited by 2,254
Shared Mental Models: Ideologies and Institutions
DENZAU, A. T., & NORTH, D. C. (1994) · Kyklos · doi:10.1111/j.1467-6435.1994.tb02246.x · Cited by 2,194
Review of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883
Genetic programming as a means for programming computers by natural selection
Koza, J. (1994) · Statistics and Computing · doi:10.1007/bf00175355 · Cited by 1,685
Evolutionary computation: Comments on the history and current state
Back, T., Hammel, U., & Schwefel, H.-P. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585888 · Cited by 1,536
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Particle Swarm Optimization: A Comprehensive Survey
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022) · IEEE Access · doi:10.1109/access.2022.3142859 · Cited by 1,584
Derivative-free optimization: a review of algorithms and comparison of software implementations
Rios, L. M., & Sahinidis, N. V. (2013) · Journal of Global Optimization · doi:10.1007/s10898-012-9951-y · Cited by 1,276
Ab initio random structure searching
Pickard, C. J., & Needs, R. J. (2011) · Journal of Physics: Condensed Matter · doi:10.1088/0953-8984/23/5/053201 · Cited by 1,302
Organizational improvisation and organizational memory
Moorman, C., & Miner, A. S. (1998) · The Academy of Management Review · doi:10.2307/259058 · Cited by 1,248
Cooperative Multi-Agent Learning: The State of the Art
Panait, L., & Luke, S. (2005) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-005-2631-2 · Cited by 1,237
A perspective view and survey of meta-learning
Vilalta, R., & Drissi, Y. (2002) · Artificial Intelligence Review · doi:10.1023/a:1019956318069 · Cited by 1,108
Niche construction, biological evolution, and cultural change
Laland, K. N., Odling-Smee, J., & Feldman, M. W. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00002417 · Cited by 1,103
The responsibility gap: Ascribing responsibility for the actions of learning automata
Matthias, A. (2004) · Ethics and Information Technology · doi:10.1007/s10676-004-3422-1 · Cited by 1,101
Sims, K. (1994) · Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94 · doi:10.1145/192161.192167 · Cited by 1,005
Using genetic algorithms to find technical trading rules
Allen, F., & Karjalainen, R. (1999) · Journal of Financial Economics · doi:10.1016/s0304-405x(98)00052-x · Cited by 855
Parameter estimation in biochemical pathways: a comparison of global optimization methods
Moles, C. G., Mendes, P., & Banga, J. R. (2003) · Genome Research · doi:10.1101/gr.1262503 · Cited by 858
Ladyman, J., Lambert, J., & Wiesner, K. (2013) · European Journal for Philosophy of Science · doi:10.1007/s13194-012-0056-8 · Cited by 846
Machine Learning Methods Without Tears: A Primer for Ecologists
Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823
Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T., Wang, L., Rogov, P., Feizi, S., Gnirke, A., Callan, C. G., Kinney, J. B., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2137 · Cited by 711
Co-evolving parasites improve simulated evolution as an optimization procedure
Hillis, W. D. (1990) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(90)90076-2 · Cited by 780
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747
Evolution of the Social Contract
Skyrms, B. (2014) · doi:10.1017/cbo9781139924825 · Cited by 743
Multi-agent simulations and ecosystem management: a review
Bousquet, F., & Le Page, C. (2004) · Ecological Modelling · doi:10.1016/j.ecolmodel.2004.01.011 · Cited by 736
Artificial evolution for computer graphics
Sims, K. (1991) · Proceedings of the 18th annual conference on Computer graphics and interactive techniques · doi:10.1145/122718.122752 · Cited by 741
On Defining Artificial Intelligence
Wang, P. (2019) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2019-0002 · Cited by 740
Siggelkow, N., & Levinthal, D. A. (2003) · Organization Science · doi:10.1287/orsc.14.6.650.24870 · Cited by 953
Hou, R., & Xia, Y. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2020.115741 · Cited by 674
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636
SPEAR, S. F., BALKENHOL, N., FORTIN, M., MCRAE, B. H., & SCRIBNER, K. (2010) · Molecular Ecology · doi:10.1111/j.1365-294x.2010.04657.x · Cited by 598
Bio inspired computing – A review of algorithms and scope of applications
Kar, A. K. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.018 · Cited by 578
Understanding pathways for scaling up health services through the lens of complex adaptive systems
Paina, L., & Peters, D. H. (2012) · Health Policy and Planning · doi:10.1093/heapol/czr054 · Cited by 534
System identification and control using genetic algorithms
Kristinsson, K., & Dumont, G. A. (1992) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/21.179842 · Cited by 517
Coordinating perceptually grounded categories through language: A case study for colour
steels, L., & belpaeme, T. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000087 · Cited by 440
Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study
Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.012 · Cited by 405
Organizational Adaptation and Environmental Selection-Interrelated Processes of Change
Levinthal, D. A. (1991) · Organization Science · doi:10.1287/orsc.2.1.140 · Cited by 401
Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks
Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387
Natural selection and the regulation of defenses
Nesse, R. M. (2005) · Evolution and Human Behavior · doi:10.1016/j.evolhumbehav.2004.08.002 · Cited by 365
Koza, J. R., Bennett, F. H., Andre, D., & Keane, M. A. (1996) · Artificial Intelligence in Design ’96 · doi:10.1007/978-94-009-0279-4_9 · Cited by 361
Comparison of Three Evolutionary Algorithms: GA, PSO, and DE
Kachitvichyanukul, V. (2012) · Industrial Engineering and Management Systems · doi:10.7232/iems.2012.11.3.215 · Cited by 355
CHALMERS, D. J., FRENCH, R. M., & HOFSTADTER, D. R. (1992) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528139208953747 · Cited by 333
Keshavarz, N., Nutbeam, D., Rowling, L., & Khavarpour, F. (2010) · Social Science & Medicine · doi:10.1016/j.socscimed.2010.01.034 · Cited by 335
Review of numerical optimization techniques for meta-device design [Invited]
Campbell, S. D., Sell, D., Jenkins, R. P., Whiting, E. B., Fan, J. A., & Werner, D. H. (2019) · Optical Materials Express · doi:10.1364/ome.9.001842 · Cited by 322
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Learning to perceive and act by trial and error
Whitehead, S. D., & Ballard, D. H. (1991) · Machine Learning · doi:10.1007/bf00058926 · Cited by 300
Gong, Y., Yang, J., & Shi, X. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101487 · Cited by 301
Function Optimization using Connectionist Reinforcement Learning Algorithms
WILLIAMS, R. J., & PENG, J. (1991) · Connection Science · doi:10.1080/09540099108946587 · Cited by 288
Dynamic and linguistic modes of complex systems
PATTEE, H. H. (1977) · International Journal of General Systems · doi:10.1080/03081077708934771 · Cited by 273
Ensemble strategies for population-based optimization algorithms – A survey
Wu, G., Mallipeddi, R., & Suganthan, P. N. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.08.015 · Cited by 270
Metaheuristic Algorithms in Modeling and Optimization
Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013) · Metaheuristic Applications in Structures and Infrastructures · doi:10.1016/b978-0-12-398364-0.00001-2 · Cited by 268
Estimating optimum parameters of tuned mass dampers using harmony search
Bekdaş, G., & Nigdeli, S. M. (2011) · Engineering Structures · doi:10.1016/j.engstruct.2011.05.024 · Cited by 259
Probabilistic Incremental Program Evolution
Salustowicz, R., & Schmidhuber, J. (1997) · Evolutionary Computation · doi:10.1162/evco.1997.5.2.123 · Cited by 249
Charging station location problem of plug-in electric vehicles
Zhu, Z.-H., Gao, Z.-Y., Zheng, J.-F., & Du, H.-M. (2016) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2016.02.002 · Cited by 223
Zheng, Y., Lu, L., Han, X., Li, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.057 · Cited by 214
Brubaker, C., Jana, S., Ray, B., Khurshid, S., & Shmatikov, V. (2014) · 2014 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2014.15 · Cited by 218
Hussein, A. F., ArunKumar, N., Ramirez-Gonzalez, G., Abdulhay, E., Tavares, J. M. R. S., & de Albuquerque, V. H. C. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.05.004 · Cited by 212
Discovery and Optimization of Materials Using Evolutionary Approaches
Le, T. C., & Winkler, D. A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00691 · Cited by 204
Biswas, P. P., Suganthan, P. N., Wu, G., & Amaratunga, G. A. J. (2019) · Renewable Energy · doi:10.1016/j.renene.2018.07.152 · Cited by 190
Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Jose, K., & Pratihar, D. K. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.02.003 · Cited by 170
(2008) · Medical Entomology and Zoology · Cited by 163
Machine learning applied to road safety modeling: A systematic literature review
Silva, P. B., Andrade, M., & Ferreira, S. (2020) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2020.07.004 · Cited by 162
SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping
Huang, C.-C., Jin, G., & Li, J. (2020) · Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3373376.3378530 · Cited by 158
Loss of plasticity in deep continual learning
Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172
“The variation and selection is reminiscent of trial-and-error processes in evolution and behaviour 44–47 and has precursors in many earlier ideas, including Keifer–Wolfowitz methods 48 and restart methods 49 in engineering and feature-search methods in machine learning 31–35 , 50 .”
Italian consumers’ income and food waste behavior
Setti, M., Falasconi, L., Segrè, A., Cusano, I., & Vittuari, M. (2016) · British Food Journal · doi:10.1108/bfj-11-2015-0427 · Cited by 145
A survey on the applications of artificial bee colony in signal, image, and video processing
Akay, B., & Karaboga, D. (2015) · Signal, Image and Video Processing · doi:10.1007/s11760-015-0758-4 · Cited by 143
Social learning strategies modify the effect of network structure on group performance
Barkoczi, D., & Galesic, M. (2016) · Nature Communications · doi:10.1038/ncomms13109 · Cited by 131
Universal darwinism as a process of bayesian inference
Campbell, J. O. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00049 · Cited by 140
Emergence in Cognitive Science
McClelland, J. L. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01116.x · Cited by 135
Ahmed, R., El Sayed, M., Arasaratnam, I., Tjong, J., & Habibi, S. (2014) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2014.2331059 · Cited by 135
Merrikh-Bayat, F. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.04.048 · Cited by 134
Artificial Intelligence Methodologies and Their Application to Diabetes
Rigla, M., García-Sáez, G., Pons, B., & Hernando, M. E. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817710475 · Cited by 131
Bekdaş, G., & Nigdeli, S. M. (2017) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2016.10.019 · Cited by 124
Alsarhan, A., Alauthman, M., Alshdaifat, E., Al-Ghuwairi, A.-R., & Al-Dubai, A. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-02963-x · Cited by 123
Organizational Evolution and the Social Ecology of Jobs
Miner, A. S. (1991) · American Sociological Review · doi:10.2307/2096255 · Cited by 121
Bayesian networks for interpretable machine learning and optimization
Mihaljević, B., Bielza, C., & Larrañaga, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.138 · Cited by 123
Metadata
- Authors
- Holland, J. H.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 1992-04-29
- OpenAlex
- W1497256448
- Cited by
- 47,002
- References
- 0
- FWCI
- 209.48
- Type
- book
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.