[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Generative Adversarial Optimization

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11655))

Included in the following conference series:

Abstract

Inspired by the adversarial learning in generative adversarial network, a novel optimization framework named Generative Adversarial Optimization (GAO) is proposed in this paper. This GAO framework sets up generative models to generate candidate solutions via an adversarial process, in which two models are trained alternatively and simultaneously, i.e., a generative model for generating candidate solutions and a discriminative model for estimating the probability that a generated solution is better than a current solution. The training procedure of the generative model is to maximize the probability of the discriminative model. Specifically, the generative model and the discriminative model are in this paper implemented by multi-layer perceptrons that can be trained by the back-propagation approach. As of an implementation of the proposed GAO, for the purpose of increasing the diversity of generated solutions, a guiding vector ever introduced in guided fireworks algorithm (GFWA) has been employed here to help constructing generated solutions for the generative model. Experiments on CEC2013 benchmark suite show that the proposed GAO framework achieves better than the state-of-art performance on multi-modal functions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 51.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 64.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)

  2. Auger, A., Hansen, N.: A restart cma evolution strategy with increasing population size. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1769–1776. IEEE (2005)

    Google Scholar 

  3. Che, T., et al.: Maximum-likelihood augmented discrete generative adversarial networks. arXiv preprint arXiv:1702.07983 (2017)

  4. Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J.: Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(3), 680–691 (2009)

    Article  Google Scholar 

  5. Chongxuan, L., Xu, T., Zhu, J., Zhang, B.: Triple generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 4088–4098 (2017)

    Google Scholar 

  6. Clerc, M.: Standard particle swarm optimisation from 2006 to 2011. Part. Swarm Cent. 253 (2011)

    Google Scholar 

  7. Dai, W., et al.: Scan: structure correcting adversarial network for chest X-rays organ segmentation. arXiv preprint arXiv:1703.08770 (2017)

  8. Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  9. Denton, E., Gross, S., Fergus, R.: Semi-supervised learning with context-conditional generative adversarial networks. arXiv preprint arXiv:1611.06430 (2016)

  10. Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486–1494 (2015)

    Google Scholar 

  11. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC 1999 (Cat. No. 99TH8406), vol. 2, pp. 1470–1477. IEEE (1999)

    Google Scholar 

  12. Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–3030 (2016)

    MathSciNet  MATH  Google Scholar 

  13. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  14. Guimaraes, G.L., Sanchez-Lengeling, B., Outeiral, C., Farias, P.L.C., Aspuru-Guzik, A.: Objective-reinforced generative adversarial networks (organ) for sequence generation models. arXiv preprint arXiv:1705.10843 (2017)

  15. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)

    Google Scholar 

  16. Hu, W.W., Tan, Y.: Generating adversarial malware examples for black-box attacks based on GAN (2017)

    Google Scholar 

  17. 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 

  18. Kennedy, J.: Swarm intelligence. In: Zomaya, A.Y. (ed.) Handbook of Nature-Inspired and Innovative Computing, pp. 187–219. Springer, Boston (2006). https://doi.org/10.1007/0-387-27705-6_6

    Chapter  Google Scholar 

  19. Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8

    Chapter  Google Scholar 

  20. Koziel, S., Yang, X.S.: Computational Optimization, Methods and Algorithms, vol. 356. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20859-1

    Book  MATH  Google Scholar 

  21. Kusner, M.J., Hernández-Lobato, J.M.: GANs for sequences of discrete elements with the Gumbel-softmax distribution. arXiv preprint arXiv:1611.04051 (2016)

  22. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  23. Lehman, J., Chen, J., Clune, J., Stanley, K.O.: Safe mutations for deep and recurrent neural networks through output gradients. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 117–124. ACM (2018)

    Google Scholar 

  24. Li, J., Tan, Y.: Loser-out tournament-based fireworks algorithm for multimodal function optimization. IEEE Trans. Evol. Comput. 22(5), 679–691 (2018)

    Article  Google Scholar 

  25. Li, J., Zheng, S., Tan, Y.: Adaptive fireworks algorithm. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3214–3221. IEEE (2014)

    Google Scholar 

  26. Li, J., Zheng, S., Tan, Y.: The effect of information utilization: Introducing a novel guiding spark in the fireworks algorithm. IEEE Trans. Evol. Comput. 21(1), 153–166 (2017)

    Article  Google Scholar 

  27. Liang, J., Qu, B., Suganthan, P., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report 201212(34), Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, pp. 281–295 (2013)

    Google Scholar 

  28. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)

    Google Scholar 

  29. Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42(4), 965–997 (2014)

    Article  Google Scholar 

  30. Nowozin, S., Cseke, B., Tomioka, R.: f-GAN: training generative neural samplers using variational divergence minimization. In: Advances in Neural Information Processing Systems, pp. 271–279 (2016)

    Google Scholar 

  31. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

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

  33. Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)

  34. 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 

  35. Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: SSGAN: secure steganography based on generative adversarial networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds.) PCM 2017. LNCS, vol. 10735, pp. 534–544. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77380-3_51

    Chapter  Google Scholar 

  36. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  37. Such, F.P., Madhavan, V., Conti, E., Lehman, J., Stanley, K.O., Clune, J.: Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567 (2017)

  38. Tan, K.C., Chiam, S.C., Mamun, A., Goh, C.K.: Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur. J. Oper. Res. 197(2), 701–713 (2009)

    Article  Google Scholar 

  39. Tan, Y.: Fireworks Algorithm. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46353-6

    Book  MATH  Google Scholar 

  40. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44

    Chapter  Google Scholar 

  41. Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: MoCoGAN: decomposing motion and content for video generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1526–1535 (2018)

    Google Scholar 

  42. Vondrick, C., Pirsiavash, H., Torralba, A.: Generating videos with scene dynamics. In: Advances In Neural Information Processing Systems, pp. 613–621 (2016)

    Google Scholar 

  43. Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems, pp. 82–90 (2016)

    Google Scholar 

  44. Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  45. Zheng, S., Janecek, A., Li, J., Tan, Y.: Dynamic search in fireworks algorithm. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3222–3229. IEEE (2014)

    Google Scholar 

  46. Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2069–2077. IEEE (2013)

    Google Scholar 

  47. Zheng, S., Li, J., Janecek, A., Tan, Y.: A cooperative framework for fireworks algorithm. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 14(1), 27–41 (2017)

    Article  Google Scholar 

  48. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the Natural Science Foundation of China (NSFC) under grant no. 61673025 and 61375119 and also Supported by Beijing Natural Science Foundation (4162029), and partially supported by National Key Basic Research Development Plan (973 Plan) Project of China under grant no. 2015CB352302.

Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Ying Tan & Bo Shi

Authors
  1. Ying Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Tan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tan, Y., Shi, B. (2019). Generative Adversarial Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26369-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26368-3

  • Online ISBN: 978-3-030-26369-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation