IEEE Computational Intelligence Magazine - May 2022 - 28

[2] T. Engel and J. Gasteiger, Chemoinformatics: Basic Concepts and Methods. Hoboken, NJ,
USA: Wiley-VCH, 2018.
[3] T. Engel and J. Gasteiger, Chemoinformatics: Achievements and Future Opportunities.
Hoboken, NJ, USA: Wiley-VCH, 2018.
[4] P. Gromski, A. Henson, J. Granda, and L. Cronin, " How to explore chemical space
using algorithms and automation, " Nat. Rev. Chem., vol. 3, pp. 119-128, 2019, doi:
10.1038/s41570-018-0066-y.
[5] H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke, " The rise of deep
learning in drug discovery, " Drug Discovery Today, vol. 23, no. 6, pp. 1241-1259, 2018, doi:
10.1016/j.drudis.2018.01.039.
[6] D. Weininger, " SMILES, a chemical language and information system. 1. Introduction
to methodology and encoding rules, " J. Chem. Inf. Comput. Sci., vol. 28, no. 1, pp.
31-36, 1988, doi: 10.1021/ci00057a005.
[7] D. Duvenaud et al., " Convolutional networks on graphs for learning molecular fingerprints, "
in Proc. Conf. Neural Inf. Process. Syst., 2015.
[8] K. Yang et al., " Analyzing learned molecular representations for property prediction, "
J. Chem. Inf. Model., vol. 59, no. 8, pp. 3370-3388, 2019, doi: 10.1021/acs.jcim.9b00237.
[9] D. Kingma and M. Welling, " Auto-encoding variational Bayes, " in Proc. Int. Conf.
Learning Representations, 2014.
[10] R. Romez-Bombarelli et al., " Automatic chemical design using a data-driven continuous
representation of molecules, " ACS Central Sci., vol. 4, pp. 268-276, 2018, doi:
10.1021/acscentsci.7b00572.
[11] M. Simonovsky and N. Komodakis, " GraphVAE: Towards generation of small graphs
using variational autoencoders, " in Proc. Int. Conf. Artif. Neural Netw., 2018, pp. 412-422.
[12] W. Jin, K. Yang, R. Barzilay, and T. Jaakkola, " Learning multimodal graph-to-graph
translation for molecular optimization, " in Proc. Int. Conf. Learning Representations, 2019.
[13] W. Jin, R. Barzilay, and T. Jaakkola, " Hierarchical generation of molecular graphs
using structural motifs, " in Proc. ICML, 2020.
[14] O. Prykhodko et al., " A de novo molecular generation method using latent vector
based generative adversarial network, " J. Cheminformat., vol. 11, p. 74, 2019, doi: 10.1186/
s13321-019-0397-9.
[15] A. Kadurin, S. Nikolenko, K. Khrabrov, A. Aliper, and A. Zhavoronkov, " druGAN:
An advanced generative adversarial autoencoder model for de novo generation of new
molecules with desired molecular properties in silico, " Mol. Pharmaceut., vol. 14, pp.
3098-3104, 2017, doi: 10.1021/acs.molpharmaceut.7b00346.
[16] M. Xu, S. Luo, Y. Bengio, J. Peng, and J. Tang, " Learning neural generative dynamics
for molecular conformation generation, " in Proc. Int. Conf. Learning Representations, 2021.
[17] Z. Zhou, S. Kearnes, L. Li, R. Zare, and P. Riley, " Optimization of molecules via
deep reinforcement learning, " Sci. Rep., vol. 9, p. 10,752, 2019.
[18] J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec, " Graph convolutional policy
network for goal-directed molecular graph generation, " in Proc. Conf. Neural Inf. Process.
Syst., 2018.
[19] M. Popova, O. Isayev, and A. Tropsha, " Deep reinforcement learning for de novo
drug design, " Sci. Adv., vol. 4, no. 7, p. eaap7885, 2018, doi: 10.1126/sciadv.aap7885.
[20] A. Eiben and J. Smith, " From evolutionary computation to the evolution of things, "
Nature, vol. 521, no. 2014, pp. 476-482, 2015.
[21] T. Besnard et al., " Automated design of ligands to polypharmacological profiles, "
Nature, vol. 412, pp. 215-220, 2012, doi: 10.1038/nature11691.
[22] K. Stanley, J. Clune, J. Lehman, and R. Miikkulainen, " Designing neural networks
through neuroevolution, " Nat. Machine Intell., vol. 1, pp. 24-30, 2019, doi: 10.1038/
s42256-018-0006-z.
[23] S. Mandal, T. Anderson, J. Gottschlich, S. Zhou, and A. Muzahid, " Learning fitness
functions for genetic algorithms, " 2019, arXiv:1908.08783.
[24] T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever, " Evolution strategies as a scalable
alternative to reinforcement learning, " 2017, arXiv:1703.03864.
[25] D. Wierstra, T. Schaul, T. Glasmachers, Y. Sun, J. Peters, and J. Schmidhuber,
" Natural evolution strategies, " J. Machine Learning Res., vol. 15, no. 2014, pp. 949-980,
2014.
[26] M. Hauschild and M. Pelikan, " An introduction and survey of estimation of distribution
algorithms, " Swarm Evolut. Comput., vol. 1, no. 3, pp. 111-128, 2011, doi: 10.1016/j.
swevo.2011.08.003.
[27] L. Perez and J. Wang, " The effectiveness of data augmentation in image classification
using deep learning, " 2017, arXiv:1712.04621.
[28] E. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. Le, " AutoAugment: Learning
augmentation strategies from data, " in Proc. Conf. Comput. Vision Pattern Recognit.,
2019.
[29] C. Shorten and T. Khoshgoftaar, " A survey on image data augmentation for deep
learning, " J. Big Data, vol. 6, p. 60, 2019, doi: 10.1186/s40537-019-0197-0.
[30] J. Wei and K. Zou, " EDA: Easy data augmentation techniques for boosting performance
on text classification tasks, " in Proc. EMNLP-IJCNLP, 2019.
[31] R. Sennrich, B. Haddow, and A. Birch, " Improving neural machine translation
models with monolingual data, " in Proc. 54th Annu. Meeting Assoc. Comput. Linguistics,
2016.
[32] G. Landrum, " RDKit: Open-source cheminformatics, " 2006. [Online]. Available:
http://www.rdkit.org
[33] M. Podda, D. Bacciu, and A. Micheli, " A deep generative model for fragment-based
molecule generation, " in Proc. Int. Conf. Artif. Intell. Statist., 2020, pp. 2240-2250.
[34] S. Shuker, P. Hajduk, R. Meadows, and S. Fesik, " Discovering high-affinity ligands
for proteins: SAR by NMR, " Science, vol. 274, no. 5292, pp. 11,531-11,534, 1996.
[35] D. Erlanson, " Introduction to fragment-based drug discovery, " Topics Curr. Chem.,
vol. 317, pp. 1-32, 2011.
[36] X. Lewell, D. Judd, S. Watson, and M. Hann, " RECAP-Retrosynthetic combinatorial
analysis procedure: A powerful new technique for identifying privileged molecular
fragments with useful applications in combinatorial chemistry, " J. Chem. Inf. Comput. Sci.,
vol. 38, no. 3, pp. 511-522, 1998, doi: 10.1021/ci970429i.
[37] J. Degen, C. Wegscheid-Gerlach, A. Zaliani, and M. Rarey, " On the art of compiling
and using 'drug-like' chemical fragment spaces, " ChemMedChem, vol. 3, no. 10, pp.
1503-1507, 2008, doi: 10.1002/cmdc.200800178.
[38] T. Mikolov, K. Chen, G. Corrado, and J. Dean, " Efficient estimation of word representations
in vector space, " in Proc. Int. Conf. Learning Representations, 2013.
[39] I. Higgins et al., " beta-VAE: Learning basic visual concepts with a constrained variational
framework, " in Proc. Int. Conf. Learning Representations, 2017.
[40] C. Yan, S. Wang, J. Yang, T. Xu, and J. Huang, " Re-balancing variational autoencoder
loss for molecule sequence generation, " in Proc. ACM Int. Conf. Bioinformat.,
Comput. Biol. Health Informat., 2020, doi: 10.1145/3388440.3412458.
[41] S. Bowman, L. Vilnis, O. Vinyals, A. Dai, R. Jozefowicz, and S. Bengio, " Generating
sentences from a continuous space, " in Proc. SIGNLL Conf. Comput. Natural Language
Learning (CoNLL), 2016, pp. 10-21.
[42] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, " A fast and elitist multiobjective
genetic algorithm: NSGA-II, " IEEE Trans. Evolutionary Comput., vol. 6, no. 2, pp.
182-197, 2002, doi: 10.1109/4235.996017.
[43] P. Bentley and J. Wakefield, " Finding acceptable solutions in the Pareto-optimal
range using multiobjective genetic algorithms, " in Soft Computing in Engineering Design and
Manufacturing, P. Chawdhry, R. Roy, and R. Pant, Eds. London, U.K.: Springer-Verlag,
1998, pp. 231-240.
[44] X. Wang and L. Cao, Genetic Algorithms - Theory, Applications, and Software Implementations.
Xi'an Jiaotong Univ. Press, 2002.
[45] F. Herrera, M. Lozano, and A. Sanchez, " A taxonomy for the crossover operator for
real-coded genetic algorithms: An experimental study, " Int. J. Intell. Syst., vol. 18, pp.
309-338, 2003, doi: 10.1002/int.10091.
[46] J. Irwin and B. Shoichet, " ZINC - A free database of commercially available compounds
for virtual screening, " J. Chem. Inf. Modeling, vol. 45, no. 1, pp. 177-182, 2005,
doi: 10.1021/ci049714+.
[47] T. Sterling and J. J. Irwin, " ZINC15 - Ligand discovery for everyone, " J. Chem. Inf.
Modeling, vol. 55, no. 11, pp. 2324-2337, 2015, doi: 10.1021/acs.jcim.5b00559.
[48] Y. Wang et al., " PubChem BioAssay: 2017 update, " Nucleic Acids Res., vol. 45, no. D1,
pp. D955-D963, 2017, doi: 10.1093/nar/gkw1118.
[49] D. Polykovskiy et al., " Molecular Sets (MOSES): A benchmarking platform for
molecular generation models, " Front. Pharmacol., vol. 11, p. 1931, 2020, doi: 10.3389/
fphar.2020.565644.
[50] G. Bickerton, G. Paolini, J. Besnard, S. Muresan, and A. Hopkins, " Quantifying
the chemical beauty of drugs, " Nat. Chem., vol. 4, pp. 90-98, 2012, doi: 10.1038/
nchem.1243.
[51] A. Goyal, A. Sordoni, M. Cote, N. Ke, and Y. Bengio, " Z-Forcing: Training stochastic
recurrent networks, " in Proc. Conf. Neural Inf. Process. Syst., 2017.
[52] X. Li, I. Kiringa, T. Yeap, X. Zhu, and Y. Li, " Anomaly detection based on unsupervised
disentangled representation learning in combination with manifold learning, " in
Proc. Int. Joint Conf. Neural Netw., 2020.
[53] S. Daulton, M. Balandat, and E. Bakshy, " Differentiable expected hypervolume improvement
for parallel multi-objective Bayesian optimization, " in Proc. Conf. Neural Inf.
Process. Syst., 2020.
[54] G. Chiandussi, M. Codegone, S. Ferrero, and F. Varesio, " Comparison of multiobjective
optimization methodologies for engineering applications, " Comput. Math. Appl.,
vol. 63, no. 5, pp. 912-942, 2012, doi: 10.1016/j.camwa.2011.11.057.
[55] R. Winter, F. Montanari, A. Steffen, H. Briem, F. Noe, and D.-A. Clevert, " Efficient
multi-objective molecular optimization in a continuous latent space, " Chem. Sci., vol. 10,
pp. 8016-8024, 2019, doi: 10.1039/C9SC01928F.
[56] J. Leguy, T. Cauchy, M. Glavatskikh, B. Duval, and B. Da Mota, " EvoMol: A
flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation, "
J. Cheminformat., vol. 12, no. 1, pp. 1-19, 2020, doi: 10.1186/s13321-02000458-z.
[57]
D. E. Clark and D. R. Westhead, " Evolutionary algorithms in computer-aided
molecular design, " J. Comput.-Aided Mol. Des., vol. 10, no. 4, pp. 337-358, 1996, doi:
10.1007/BF00124503.
[58] A. L. Parrill, " Evolutionary and genetic methods in drug design, " Drug Discovery
Today, vol. 1, no. 12, pp. 514-521, 1996, doi: 10.1016/S1359-6446(96)10045-3.
[59] P. Willett, " Genetic algorithms in molecular recognition and design, " Trends Biotechnol.,
vol. 13, no. 12, pp. 516-521, 1995, doi: 10.1016/S0167-7799(00)89015-0.
[60] A. Nigam, P. Friederich, M. Krenn, and A. Aspuru-Guzik, " Augmenting genetic
algorithms with deep neural networks for exploring the chemical space, " in Proc. Int.
Conf. Learning Representations, 2020.
[61] Y. Tian, R. Cheng, X. Zhang, M. Li, and Y. Jin, " Diversity assessment of multiobjective
evolutionary algorithms: Performance metric and benchmark problems, " IEEE
Comput. Intell. Mag., vol. 14, no. 3, pp. 61-74, Aug. 2019, doi: 10.1109/MCI.2019.
2919398.
[62] K. Atz, F. Grisoni, and G. Schneider, " Geometric deep learning on molecular representations, "
Nat. Mach. Intell., vol. 3, pp. 1023-1032, 2021, doi: 10.1038/s42256-02100418-8.
[63]
S. Luo, J. Guan, J. Ma, and J. Peng, " A 3D generative model for structure-based drug
design, " in Proc. Int. Conf. Neural Inf. Process. Syst., 2021.
28 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2022
http://www.rdkit.org

IEEE Computational Intelligence Magazine - May 2022

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - May 2022

Contents
IEEE Computational Intelligence Magazine - May 2022 - Cover1
IEEE Computational Intelligence Magazine - May 2022 - Cover2
IEEE Computational Intelligence Magazine - May 2022 - Contents
IEEE Computational Intelligence Magazine - May 2022 - 2
IEEE Computational Intelligence Magazine - May 2022 - 3
IEEE Computational Intelligence Magazine - May 2022 - 4
IEEE Computational Intelligence Magazine - May 2022 - 5
IEEE Computational Intelligence Magazine - May 2022 - 6
IEEE Computational Intelligence Magazine - May 2022 - 7
IEEE Computational Intelligence Magazine - May 2022 - 8
IEEE Computational Intelligence Magazine - May 2022 - 9
IEEE Computational Intelligence Magazine - May 2022 - 10
IEEE Computational Intelligence Magazine - May 2022 - 11
IEEE Computational Intelligence Magazine - May 2022 - 12
IEEE Computational Intelligence Magazine - May 2022 - 13
IEEE Computational Intelligence Magazine - May 2022 - 14
IEEE Computational Intelligence Magazine - May 2022 - 15
IEEE Computational Intelligence Magazine - May 2022 - 16
IEEE Computational Intelligence Magazine - May 2022 - 17
IEEE Computational Intelligence Magazine - May 2022 - 18
IEEE Computational Intelligence Magazine - May 2022 - 19
IEEE Computational Intelligence Magazine - May 2022 - 20
IEEE Computational Intelligence Magazine - May 2022 - 21
IEEE Computational Intelligence Magazine - May 2022 - 22
IEEE Computational Intelligence Magazine - May 2022 - 23
IEEE Computational Intelligence Magazine - May 2022 - 24
IEEE Computational Intelligence Magazine - May 2022 - 25
IEEE Computational Intelligence Magazine - May 2022 - 26
IEEE Computational Intelligence Magazine - May 2022 - 27
IEEE Computational Intelligence Magazine - May 2022 - 28
IEEE Computational Intelligence Magazine - May 2022 - 29
IEEE Computational Intelligence Magazine - May 2022 - 30
IEEE Computational Intelligence Magazine - May 2022 - 31
IEEE Computational Intelligence Magazine - May 2022 - 32
IEEE Computational Intelligence Magazine - May 2022 - 33
IEEE Computational Intelligence Magazine - May 2022 - 34
IEEE Computational Intelligence Magazine - May 2022 - 35
IEEE Computational Intelligence Magazine - May 2022 - 36
IEEE Computational Intelligence Magazine - May 2022 - 37
IEEE Computational Intelligence Magazine - May 2022 - 38
IEEE Computational Intelligence Magazine - May 2022 - 39
IEEE Computational Intelligence Magazine - May 2022 - 40
IEEE Computational Intelligence Magazine - May 2022 - 41
IEEE Computational Intelligence Magazine - May 2022 - 42
IEEE Computational Intelligence Magazine - May 2022 - 43
IEEE Computational Intelligence Magazine - May 2022 - 44
IEEE Computational Intelligence Magazine - May 2022 - 45
IEEE Computational Intelligence Magazine - May 2022 - 46
IEEE Computational Intelligence Magazine - May 2022 - 47
IEEE Computational Intelligence Magazine - May 2022 - 48
IEEE Computational Intelligence Magazine - May 2022 - 49
IEEE Computational Intelligence Magazine - May 2022 - 50
IEEE Computational Intelligence Magazine - May 2022 - 51
IEEE Computational Intelligence Magazine - May 2022 - 52
IEEE Computational Intelligence Magazine - May 2022 - 53
IEEE Computational Intelligence Magazine - May 2022 - 54
IEEE Computational Intelligence Magazine - May 2022 - 55
IEEE Computational Intelligence Magazine - May 2022 - 56
IEEE Computational Intelligence Magazine - May 2022 - 57
IEEE Computational Intelligence Magazine - May 2022 - 58
IEEE Computational Intelligence Magazine - May 2022 - 59
IEEE Computational Intelligence Magazine - May 2022 - 60
IEEE Computational Intelligence Magazine - May 2022 - 61
IEEE Computational Intelligence Magazine - May 2022 - 62
IEEE Computational Intelligence Magazine - May 2022 - 63
IEEE Computational Intelligence Magazine - May 2022 - 64
IEEE Computational Intelligence Magazine - May 2022 - 65
IEEE Computational Intelligence Magazine - May 2022 - 66
IEEE Computational Intelligence Magazine - May 2022 - 67
IEEE Computational Intelligence Magazine - May 2022 - 68
IEEE Computational Intelligence Magazine - May 2022 - 69
IEEE Computational Intelligence Magazine - May 2022 - 70
IEEE Computational Intelligence Magazine - May 2022 - 71
IEEE Computational Intelligence Magazine - May 2022 - 72
IEEE Computational Intelligence Magazine - May 2022 - 73
IEEE Computational Intelligence Magazine - May 2022 - 74
IEEE Computational Intelligence Magazine - May 2022 - 75
IEEE Computational Intelligence Magazine - May 2022 - 76
IEEE Computational Intelligence Magazine - May 2022 - 77
IEEE Computational Intelligence Magazine - May 2022 - 78
IEEE Computational Intelligence Magazine - May 2022 - 79
IEEE Computational Intelligence Magazine - May 2022 - 80
IEEE Computational Intelligence Magazine - May 2022 - 81
IEEE Computational Intelligence Magazine - May 2022 - 82
IEEE Computational Intelligence Magazine - May 2022 - 83
IEEE Computational Intelligence Magazine - May 2022 - 84
IEEE Computational Intelligence Magazine - May 2022 - 85
IEEE Computational Intelligence Magazine - May 2022 - 86
IEEE Computational Intelligence Magazine - May 2022 - 87
IEEE Computational Intelligence Magazine - May 2022 - 88
IEEE Computational Intelligence Magazine - May 2022 - 89
IEEE Computational Intelligence Magazine - May 2022 - 90
IEEE Computational Intelligence Magazine - May 2022 - 91
IEEE Computational Intelligence Magazine - May 2022 - 92
IEEE Computational Intelligence Magazine - May 2022 - 93
IEEE Computational Intelligence Magazine - May 2022 - 94
IEEE Computational Intelligence Magazine - May 2022 - 95
IEEE Computational Intelligence Magazine - May 2022 - 96
IEEE Computational Intelligence Magazine - May 2022 - 97
IEEE Computational Intelligence Magazine - May 2022 - 98
IEEE Computational Intelligence Magazine - May 2022 - 99
IEEE Computational Intelligence Magazine - May 2022 - 100
IEEE Computational Intelligence Magazine - May 2022 - 101
IEEE Computational Intelligence Magazine - May 2022 - 102
IEEE Computational Intelligence Magazine - May 2022 - 103
IEEE Computational Intelligence Magazine - May 2022 - 104
IEEE Computational Intelligence Magazine - May 2022 - Cover3
IEEE Computational Intelligence Magazine - May 2022 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202311
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202308
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202305
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202302
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202211
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202208
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202205
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202202
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202111
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202108
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202105
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202102
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202011
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202008
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202005
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202002
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201911
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201908
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201905
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201902
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201811
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201808
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201805
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201802
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter12
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall12
https://www.nxtbookmedia.com