1. Al-Dujaili, A., Schmiedlechner, T., Hemberg, E., O’Reilly, U.M.: Towards distributed coevolutionary GANs. In: AAAI 2018 Fall Symposium (2018)

   Google Scholar 

2. Antonio, L.M., Coello, C.A.C.: Coevolutionary multiobjective evolutionary algorithms: survey of the state-of-the-art. IEEE Trans. Evol. Comput.22(6), 851–865 (2018)

   Article  Google Scholar 

3. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223 (2017)

   Google Scholar 

4. Barratt, S., Sharma, R.: A note on the inception score. Preprint, arXiv:1801.01973 (2018)

   Google Scholar 

5. Berthelot, D., Schumm, T., Metz, L.: BEGAN: Boundary equilibrium generative adversarial networks. Preprint, arXiv:1703.10717 (2017)

   Google Scholar 

6. Borji, A.: Pros and cons of GAN evaluation measures. Comput. Vis. Image Underst.179, 41–65 (2019)

   Article  Google Scholar 

7. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, 6–9 May 2019

   Google Scholar 

8. Coello, C.A.C., Sierra, M.R.: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Mexican International Conference on Artificial Intelligence, pp. 688–697. Springer, Berlin (2004)

   Google Scholar 

9. Costa, V., Lourenço, N., Correia, J., Machado, P.: COEGAN: evaluating the coevolution effect in generative adversarial networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 374–382. ACM, New York (2019)

   Google Scholar 

10. Costa, V., Lourenço, N., Machado, P.: Coevolution of generative adversarial networks. In: International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pp. 473–487. Springer, Berlin (2019)

    Chapter  Google Scholar 

11. Durugkar, I., Gemp, I., Mahadevan, S.: Generative multi-adversarial networks. Preprint, arXiv:1611.01673 (2016)

    Google Scholar 

12. Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. Preprint, arXiv:1706.07068 (2017)

    Google Scholar 

13. Fedus, W., Rosca, M., Lakshminarayanan, B., Dai, A.M., Mohamed, S., Goodfellow, I.: Many paths to equilibrium: GANs do not need to decrease a divergence at every step. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, 30 April–3 May 2018, Conference Track Proceedings (2018)

    Google Scholar 

14. Garciarena, U., Santana, R., Mendiburu, A.: Evolved GANs for generating Pareto set approximations. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’18, pp. 434–441. ACM, New York (2018)

    Google Scholar 

15. Ghosh, A., Kulharia, V., Namboodiri, V.P., Torr, P.H., Dokania, P.K.: Multi-agent diverse generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8513–8521 (2018)

    Google Scholar 

16. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

17. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5769–5779 (2017)

    Google Scholar 

18. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local NASH equilibrium. In: Advances in Neural Information Processing Systems, pp. 6629–6640 (2017)

    Google Scholar 

19. Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D42(1–3), 228–234 (1990)

    Article  Google Scholar 

20. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

21. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, 30 April–3 May 2018, Conference Track Proceedings (2018)

    Google Scholar 

22. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. Preprint, arXiv:1812.04948 (2018)

    Google Scholar 

23. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, 7–9 May 2015, Conference Track Proceedings (2015)

    Google Scholar 

24. LeCun, Y.: The MNIST database of handwritten digits.http://yann.lecun.com/exdb/mnist/ (1998)

25. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)

    Google Scholar 

26. Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs created equal? A large-scale study. Preprint, arXiv:1711.10337 (2017)

    Google Scholar 

27. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2813–2821. IEEE, Piscataway (2017)

    Google Scholar 

28. Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Navruzyan, A., Duffy, N., Hodjat, B.: Evolving deep neural networks. Preprint, arXiv:1703.00548 (2017)

    Google Scholar 

29. Mirza, M., Osindero, S.: Conditional generative adversarial nets. Preprint, arXiv:1411.1784 (2014)

    Google Scholar 

30. Mitchell, M.: Coevolutionary learning with spatially distributed populations. In: Computational Intelligence: Principles and Practice, pp. 137–154. IEEE Computational Intelligence Society, Piscataway (2006)

    Google Scholar 

31. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, 30 April–3 May 2018, Conference Track Proceedings (2018)

    Google Scholar 

32. Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2642–2651. JMLR.org (2017)

    Google Scholar 

33. Olsson, C., Bhupatiraju, S., Brown, T., Odena, A., Goodfellow, I.: Skill rating for generative models. Preprint, arXiv:1808.04888 (2018)

    Google Scholar 

34. Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y.: Recent progress on generative adversarial networks (GANs): A survey. IEEE Access7, 36322–36333 (2019)

    Article  Google Scholar 

35. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint, arXiv:1511.06434 (2015)

    Google Scholar 

36. Rawal, A., Rajagopalan, P., Miikkulainen, R.: Constructing competitive and cooperative agent behavior using coevolution. In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), pp. 107–114 (2010)

    Google Scholar 

37. Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. Preprint, arXiv:1605.05396 (2016)

    Google Scholar 

38. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis.115(3), 211–252 (2015)

    Article MathSciNet  Google Scholar 

39. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)

    Google Scholar 

40. Schäfer, F., Anandkumar, A.: Competitive gradient descent. Preprint, arXiv:1905.12103 (2019)

    Google Scholar 

41. Sims, K.: Evolving 3d morphology and behavior by competition. Artif. Life1(4), 353–372 (1994)

    Article  Google Scholar 

42. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput.10(2), 99–127 (2002)

    Article  Google Scholar 

43. Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. J. Artif. Intell. Res.21, 63–100 (2004)

    Article  Google Scholar 

44. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

45. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

46. Toutouh, J., Hemberg, E., O’Reilly, U.M.: Spatial evolutionary generative adversarial networks. Preprint, arXiv:1905.12702 (2019)

    Google Scholar 

47. Wang, C., Xu, C., Yao, X., Tao, D.: Evolutionary generative adversarial networks. Preprint, arXiv:1803.00657 (2018)

    Google Scholar 

48. Wang, Z., She, Q., Ward, T.E.: Generative adversarial networks: a survey and taxonomy. Preprint, arXiv:1906.01529 (2019)

    Google Scholar 

49. Xu, Q., Huang, G., Yuan, Y., Guo, C., Sun, Y., Wu, F., Weinberger, K.: An empirical study on evaluation metrics of generative adversarial networks. Preprint, arXiv:1806.07755 (2018)

    Google Scholar 

50. Yao, X.: Evolving artificial neural networks. Proc. IEEE87(9), 1423–1447 (1999)

    Article  Google Scholar 

51. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. Preprint, arXiv:1805.08318 (2018)

    Google Scholar 

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