Abstract
In this opening chapter, we overview the quickly developing field of evolutionary machine learning. We first motivate the field and define how we understand evolutionary machine learning. Then we take a look at its roots, finding that it has quite a long history, going back to the 1950s. We introduce a taxonomy of the field, discuss the major branches of evolutionary machine learning, and conclude by outlining open problems.
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References
Ackley, D., Littman, M.: Interactions between learning and evolution. In: Langton, C., Taylor, C., Farmer, J., Rasmussen, S (eds.) Artificial Life II, pp. 487–509. Addison-Wesley (1991)
Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., Tran, B., Xue, B., Zhang, M.: A survey on evolutionary machine learning. J. Royal Soc. New Zealand 49(2), 205–228 (2019)
Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., Zimmermann, T.: Software engineering for machine learning: A case study. In: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291–300. IEEE (2019)
Ashby, W.R.: An Introduction to Cybernetics. Chapman and Hall (1956)
Bailey, B.: The Impact Of Moore’s Law Ending (2018). https://cacm.acm.org/news/232532-the-impact-of-moores-law-ending/fulltext (Last accessed Oct 12 2022)
Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connect. Sci. 6, 325–354 (1994)
Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming–An Introduction. Morgan Kaufmann (1998)
Barbiero, P., Squillero, G., Tonda, A.: Modeling generalization in machine learning: A methodological and computational study. ArXiv preprint arXiv:2006.15680 (2020)
Bedau, M.A.: Weak emergence. Philos. Perspect. 11, 375–399 (1997)
Belle, V., Papantonis, I.: Principles and practice of explainable machine learning. Front. Big Data 39 (2021)
Beniaguev, D., Segev, I., London, M.: Single cortical neurons as deep artificial neural networks. Neuron 109(17), 2727–2739 (2021)
Benthall, S.: Don’t fear the reaper: Refuting Bostrom’s superintelligence argument. ArXiv preprint arXiv:1702.08495 (2017)
Blasch, E., Pham, T., Chong, C.-Y., Koch, W., Leung, H., Braines, D., Abdelzaher, T.: Machine learning/artificial intelligence for sensor data fusion-opportunities and challenges. IEEE Aerosp. Electron. Syst. Mag. 36(7), 80–93 (2021)
Bostrom, N.: Superintelligence. Oxford University Press (2016)
Callebaut, W., Rasskin-Gutman, D., Simon, H.A.: Modularity: Understanding the Development and Evolution of Natural Complex Systems. MIT Press (2005)
Chalmers, D.J.: The evolution of learning: An experiment in genetic connectionism. In: Connectionist Models, pp. 81–90. Elsevier (1991)
Coello, C.C.: Evolutionary multi-objective optimization: A historical view of the field. IEEE Comput. Intell. Mag. 1(1), 28–36 (2006)
Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: Proceedings of the First International Conference on Genetic Algorithms (ICGA-1985), pp. 183–187 (1985)
Dai, T., Sycara, K., Zheng, R.: Agent reasoning in AI-powered negotiation. In: Handbook of Group Decision and Negotiation, pp. 1187–1211 (2021)
D’Ambrosio, D.B., Stanley, K.O.: A novel generative encoding for exploiting neural network sensor and output geometry. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 974–981 (2007)
de Ágreda, Á.G.: Ethics of autonomous weapons systems and its applicability to any ai systems. Telecommun. Policy 44(6), 101953 (2020)
Dong, X., Yu, Z., Cao, W., Shi, Y., Ma, Q.: A survey on ensemble learning. Front. Comput. Sci. 14, 241–258 (2020)
Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Commun. ACM 63(1), 68–77 (2019)
Eden, A.H., Moor, J.H., Søraker, J.H., Steinhart, E.: Singularity Hypotheses. The Frontiers Collection. Springer, Berlin (2012)
Emes, R.D., Grant, S.G.: Evolution of synapse complexity and diversity. Ann. Rev. Neurosci. 35, 111–131 (2012)
Engelbart, D.: Augmenting Human Intellect: A Conceptual Framework, Summary Report. Technical Report AFOSR-3233, Stanford Research Institute, Menlo Park, CA (1962)
Englander, A.C.: Machine learning of visual recognition using genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 197–201. Erlbaum Associates Inc, USA (1985)
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., Pritzel, A., Wierstra, D.: Pathnet: Evolution channels gradient descent in super neural networks. ArXiv preprint arXiv:1701.08734 (2017)
Fernando, C., Szathmáry, E., Husbands, P.: Selectionist and evolutionary approaches to brain function: a critical appraisal. Front. Comput. Neurosci. 6, 24 (2012)
Fogel, L., Owens, A., Walsh, M.: Artificial Intelligence through Simulated Evolution. Wiley, Chichester, WS, UK (1966)
Fogel, L.J.: Autonomous automata. Ind. Res. 4, 14–19 (1962)
Forsyth, R.: BEAGLE-A Darwinian approach to pattern recognition. Kybernetes 10, 159–166 (1981)
Fox, D.: The limits of intelligence. Sci. Am. 305(1), 36–43 (2011)
Fraser, A.S.: Simulation of genetic systems by automatic digital computers i. introduction. Australian J. Biol. Sci. 10(4), 484–491 (1957)
Friedberg, R.M.: A learning machine: Part i. IBM J. Res. Develop. 2(1), 2–13 (1958)
Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)
Gagné, C., Schoenauer, M., Sebag, M., Tomassini, M.: Genetic programming for kernel-based learning with co-evolving subsets selection. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) Parallel Problem Solving from Nature-PPSN IX, pp. 1008–1017. Springer, Berlin (2006)
Goldberg, D.E.: Computer-aided Gas Pipeline Operation using Genetic Algorithms and Rule Learning. University of Michigan (1983)
Gong, Y.-J., Chen, W.-N., Zhan, Z.-H., Zhang, J., Li, Y., Zhang, Q., Li, J.-J.: Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl. Soft Comput. 34, 286–300 (2015)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: 27th Advances in Neural Information Processing Systems Conference (2014)
Gruau, F.: Genetic micro programming of neural networks. In: Kinnear, K.E., Jr. (ed.) Advances in Genetic Programming, pp. 495–518. MIT Press (1994)
Harris, W.: Zero to Brain. Princeton University Press (2022)
Hassan, M.B., Saeed, R.A., Khalifa, O., Ali, E.S., Mokhtar, R.A., Hashim, A.A.: Green machine learning for green cloud energy efficiency. In: 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), pp. 288–294. IEEE (2022)
Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., Pineau, J.: Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21(1), 10039–10081 (2020)
Holland, J.H.: Outline for a logical theory of adaptive systems. J. ACM 9(3), 297–314 (1962)
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA (1975)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. 63, 49 (1977)
Howley, T., Madden, M.G.: The genetic kernel support vector machine: Description and evaluation. Artif. Intell. Rev. 24(3–4), 379–395 (2005)
Jabbar, A., Li, X., Omar, B.: A survey on generative adversarial networks: Variants, applications, and training. ACM Comput. Surv. (CSUR) 54(8), 1–49 (2021)
Kelly, S., Smith, R.J., Heywood, M.I., Banzhaf, W.: Emergent tangled program graphs in partially observable recursive forecasting and vizdoom navigation tasks. ACM Trans. Evol. Learn. Optim. 1(3), 1–41 (2021)
Kim, H.B., Jung, S.H., Kim, T.G., Park, K.H.: Fast learning method for back-propagation neural network by evolutionary adaptation of learning rates. Neurocomputing 11(1), 101–106 (1996)
Koeppe, P., Hamann, C.: A program for non-linear regression analysis to be used on desk-top computers. Comput. Program. Biomed. 12(2–3), 121–128 (1980)
Korot, E., Guan, Z., Ferraz, D., Wagner, S.K., Zhang, G., Liu, X., Faes, L., Pontikos, N., Finlayson, S.G., Khalid, H., et al.: Code-free deep learning for multi-modality medical image classification. Nat. Mach. Intell. 3(4), 288–298 (2021)
Koza, J.R.: Hierarchical genetic algorithms operating on populations of computer programs. In: International Joint Conference on Artifcial Intelligence (IJCAI-89), vol. 89, pp. 768–774 (1989)
Koza, J.R.: Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical report, Department of Computer Science, Stanford University, Stanford, CA, USA (1990)
Koza, J.R.: Genetic Programming–On the Programming of Computers by Means of Natural Selection. MIT Press (1992)
Lacoste, A., Luccioni, A., Schmidt, V., Dandres, T.: Quantifying the carbon emissions of machine learning. ArXiv preprint arXiv:1910.09700 (2019)
Lehman, J., Clune, J., Misevic, D., et al.: The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities. Artif. Life 26(2), 274–306 (2020)
Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G.G., Tan, K.C.: A survey on evolutionary neural architecture search. IEEE Trans. Neural Networks Learn. Syst. 34, 550–570 (2021)
Lo Piano, S.: Ethical principles in machine learning and artificial intelligence: Cases from the field and possible ways forward. Humanit. Soc. Sci. Commun. 7(1), 1–7 (2020)
Lu, Z., Cheng, R., Jin, Y., Tan, K.C., Deb, K.: Neural architecture search as multiobjective optimization benchmarks: Problem formulation and performance assessment. ArXiv preprint arXiv:2208.04321 (2022)
Lu, Z., Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., Banzhaf, W.: NSGA-Net: Neural Architecture Search using Multi-objective Genetic Algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 419–427 (2019)
Ma, X., Li, X., Zhang, Q., Tang, K., Liang, Z., Xie, W., Zhu, Z.: A survey on cooperative co-evolutionary algorithms. IEEE Trans. Evol. Comput. 23(3), 421–441 (2018)
Maas, M.M.: How viable is international arms control for military artificial intelligence? three lessons from nuclear weapons. Contemp. Secur. Policy 40(3), 285–311 (2019)
Machado, P., Baeta, F., Martins, T., Correia, J.: GP-based generative adversarial models. In: Trujillo, L., Winkler, S.M., Silva, S., Banzhaf, W. (eds.) Genetic Programming Theory and Practice XIX, Genetic and Evolutionary Computation, pp. 117–140. Springer, Ann Arbor, USA (2022)
Machado, P., Romero, J., Manaris, B.Z.: Experiments in computational aesthetics. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, Natural Computing Series, pp. 381–415. Springer (2008)
Mei, Y., Chen, Q., Lensen, A., Xue, B., Zhang, M.: Explainable artificial intelligence by genetic programming: A survey. IEEE Trans. Evol. Comput. 27(3), 621–641 (2023)
Miller, J.F.: Designing Multiple ANNs with Evolutionary Development: Activity Dependence. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds.) Genetic Programming Theory and Practice XVIII, pp. 165–180. Springer Nature Singapore, Singapore (2022)
Montana, D.J., Davis, L., et al.: Training feedforward neural networks using genetic algorithms. In: International Joint Conference on Artifcial Intelligence (IJCAI-89), vol. 89, pp. 762–767 (1989)
Nguyen, A.M., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CoRR, arXiv:1412.1897 (2014)
Nolfi, S., Floreano, D.: Learning and evolution. Autonom. Robot. 7, 89–113 (1999)
Olson, R.S., Moore, J.H.: Tpot: A tree-based pipeline optimization tool for automating machine learning. In: Workshop on Automatic Machine Learning, pp. 66–74. PMLR (2016)
Paleyes, A., Urma, R.-G., Lawrence, N.D.: Challenges in deploying machine learning: a survey of case studies. ACM Comput. Surv. 55(6), 1–29 (2022)
Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: A review. Neural Networks 113, 54–71 (2019)
Peng, H.: A comprehensive overview and survey of recent advances in meta-learning. ArXiv preprint arXiv:2004.11149 (2020)
Pessach, D., Shmueli, E.: A review on fairness in machine learning. ACM Comput. Surv. (CSUR) 55(3), 1–44 (2022)
Phanendra Babu, G., Narasimha Murty, M.: A near-optimal initial seed value selection in k-means means algorithm using a genetic algorithm. Patt. Recogn. Lett. 14(10), 763–769 (1993)
Pujari, K.N., Miriyala, S.S., Mittal, P., Mitra, K.: Better wind forecasting using evolutionary neural architecture search driven green deep learning. Expert Syst. Appl. 214, 119063 (2023)
Qiao, Y., Zhao, L., Luo, C., Luo, Y., Wu, Y., Li, S., Bu, D., Zhao, Y.: Multi-modality artificial intelligence in digital pathology. Brief. Bioinform. 23(6), bbac367 (2022)
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. CoRR arXiv:2103.00020 (2021)
Raghavan, V.V., Birchard, K.: A clustering strategy based on a formalism of the reproductive process in natural systems. In: Proceedings of the 2nd Annual International ACM SIGIR Conference on Information Storage and Retrieval: Information Implications into the Eighties, SIGIR ’79, pp. 10–22. Association for Computing Machinery , New York, NY, USA (1979)
Real, E., Liang, C., So, D., Le, Q.: Automl-zero: Evolving machine learning algorithms from scratch. In: International Conference on Machine Learning, pp. 8007–8019. PMLR (2020)
Rechenberg, I.: Cybernetic solution path of an experimental problem. Technical Report Library Translation No. 1122, Royal Aircraft Establishment, Farnborough (1965)
Sagi, O., Rokach, L.: Ensemble learning: A survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 8(4), e1249 (2018)
Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning (2017)
Salvato, E., Fenu, G., Medvet, E., Pellegrino, F.A.: Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning. IEEE Access 9, 153171–153187 (2021)
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Develop. 3(3), 210–229 (1959)
Schaffer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. Technical report, Vanderbilt Univ., Nashville, TN (USA) (1985)
Shinozaki, T., Watanabe, S.: Structure discovery of deep neural network based on evolutionary algorithms. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4979–4983. IEEE (2015)
Simon, H.A.: The Sciences of the Artificial, Reissue of the third edition with a New Introduction by John Laird. MIT Press (2019)
Sims, K.: Evolving 3d morphology and behavior by competition. Artif. Life 1(4), 353–372 (1994)
Sims, K.: Evolving virtual creatures. In: Schweitzer, D., Glassner, A.S., Keeler, M. (eds.) Proceedings of the 21th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1994, Orlando, FL, USA, July 24-29, 1994, pp. 15–22. ACM (1994)
So, D., Le, Q., Liang, C.: The evolved transformer. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 5877–5886 (2019)
Spector, L., Alpern, A.: Induction and recapitulation of deep musical structure. In: Proceedings of the IFCAI–95 Workshop on Artificial Intelligence and Music, pp. 41–48 (1995)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
Stepney, S.: Life as a cyber-bio-physical system. In: Trujillo, L., Winkler, S., Silva, S., Banzhaf, W. (eds.) Genetic Programming–Theory and Practice. GPTP XIX, pp. 167–200. Springer Nature, Singapore (2023)
Sze, V.: Designing hardware for machine learning: The important role played by circuit designers. IEEE Solid-State Circ. Mag. 9(4), 46–54 (2017)
Telikani, A., Tahmassebi, A., Banzhaf, W., Gandomi, A.H.: Evolutionary machine learning: A survey. ACM Comput. Surv. 54(8) (2021)
Tkachenko, A., Brovinskaya, N., Kondratenko, Y.: Evolutionary adaptation of control processes in robots operating in nonstationary environments. Mech. Mach. Theory 18(4), 275–278 (1983)
Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., Hüllermeier, E.: Towards green automated machine learning: Status quo and future directions. ArXiv preprint arXiv:2111.05850 (2021)
Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)
Turing, A.M.: Intelligent machinery, a heretical theory. In: The Essential Turing. Oxford University Press (2004)
van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(86), 2579–2605 (2008)
Wang, J.X.: Meta-learning in natural and artificial intelligence. Current Opin. Behav. Sci. 38, 90–95 (2021)
Wang, Z., Gao, S., Wang, J., Yang, H., Todo, Y.: A dendritic neuron model with adaptive synapses trained by differential evolution algorithm. In: Computational Intelligence and Neuroscience (2020)
Weiss, E.: Arthur Lee Samuel (1901–90). IEEE Ann. History Comput. 14(3), 55–69 (1992)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016)
Yao, X.: Evolutionary artificial neural networks. Int. J. Neural Syst. 4(3), 203–222 (1993)
Yao, X.: Evolving artificial neural networks. Proceed. IEEE 87(9), 1423–1447 (1999)
Yerushalmi, U., Teicher, M.: Evolving synaptic plasticity with an evolutionary cellular development model. PLoS ONE 3(11), e3697 (2008)
Zhang, Q., Liu, Y., Blum, R.S., Han, J., Tao, D.: Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review. Inf. Fusion 40, 57–75 (2018)
Zhou, Y., Kantarcioglu, M., Xi, B.: A survey of game theoretic approach for adversarial machine learning. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 9(3), e1259 (2019)
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proceed. IEEE 109(1), 43–76 (2020)
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This work is funded by the FCT–Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit–UIDB/00326/2020 or project code UIDP/00326/2020.
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Banzhaf, W., Machado, P. (2024). Fundamentals of Evolutionary Machine Learning. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_1
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