A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in 3rd International Conference on Learning Representations (ICLR 2015), (2015).

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

S. Thibault, C. Gagné, J. Beaulieu, and M. Parizeau, “Evolutionary algorithms applied to lens design: case study and analysis,” in Optical Design and Engineering II, vol. 5962 (International Society for Optics and Photonics, 2005).

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015)..

[Crossref]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT University, 2016).

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The Handbook of Brain Theory and Neural Networks (MIT Press, 1995), pp. 255–258.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

G. Côté, J.-F. Lalonde, and S. Thibault, “Toward Training a Deep Neural Network to Optimize Lens Designs,” in Frontiers in Optics / Laser Science, (Optical Society of America, 2018), p. JW4A.28.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT University, 2016).

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

G. W. Forbes and A. E. W. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 144–154.

S. Thibault, C. Gagné, J. Beaulieu, and M. Parizeau, “Evolutionary algorithms applied to lens design: case study and analysis,” in Optical Design and Engineering II, vol. 5962 (International Society for Optics and Photonics, 2005).

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT University, 2016).

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics Speech and Signal Processing (2013), pp. 6645–6649.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015)..

[Crossref]

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics Speech and Signal Processing (2013), pp. 6645–6649.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, (Curran Associates, Inc., 2012), pp. 1097–1105.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens Design: Global Optimization with Escape Function,” Opt. Rev. 2(6), 463–470 (1995).

[Crossref]

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput. 9(8), 1735–1780 (1997).

[Crossref]

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-Normalizing Neural Networks,” in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds. (Curran Associates, Inc., 2017) pp. 971–980.

K. Höschel and V. Lakshminarayanan, “Genetic algorithms for lens design: a review,” J. Opt. 2, 463–470 (2018).

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens Design: Global Optimization with Escape Function,” Opt. Rev. 2(6), 463–470 (1995).

[Crossref]

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens Design: Global Optimization with Escape Function,” Opt. Rev. 2(6), 463–470 (1995).

[Crossref]

G. W. Forbes and A. E. W. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 144–154.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in 3rd International Conference on Learning Representations (ICLR 2015), (2015).

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-Normalizing Neural Networks,” in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds. (Curran Associates, Inc., 2017) pp. 971–980.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, (Curran Associates, Inc., 2012), pp. 1097–1105.

K. Höschel and V. Lakshminarayanan, “Genetic algorithms for lens design: a review,” J. Opt. 2, 463–470 (2018).

G. Côté, J.-F. Lalonde, and S. Thibault, “Toward Training a Deep Neural Network to Optimize Lens Designs,” in Frontiers in Optics / Laser Science, (Optical Society of America, 2018), p. JW4A.28.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” in Advances in Neural Information Processing Systems 27, (Curran Associates, Inc., 2014), pp. 3104–3112.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015)..

[Crossref]

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The Handbook of Brain Theory and Neural Networks (MIT Press, 1995), pp. 255–258.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-Normalizing Neural Networks,” in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds. (Curran Associates, Inc., 2017) pp. 971–980.

C. Menke, “Application of particle swarm optimization to the automatic design of optical systems,” in Optical Design and Engineering VII, vol. 10690 (International Society for Optics and Photonics, 2018).

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics Speech and Signal Processing (2013), pp. 6645–6649.

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens Design: Global Optimization with Escape Function,” Opt. Rev. 2(6), 463–470 (1995).

[Crossref]

D. Sturlesi and D. C. O’Shea, “Future of global optimization in optical design,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 54–69

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens Design: Global Optimization with Escape Function,” Opt. Rev. 2(6), 463–470 (1995).

[Crossref]

S. Thibault, C. Gagné, J. Beaulieu, and M. Parizeau, “Evolutionary algorithms applied to lens design: case study and analysis,” in Optical Design and Engineering II, vol. 5962 (International Society for Optics and Photonics, 2005).

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

S. Shamshirband, D. Petković, N. T. Pavlović, S. Ch, T. A. Altameem, and A. Gani, “Support vector machine firefly algorithm based optimization of lens system,” Appl. Opt. 54(1), 37–45 (2015).

[Crossref]

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

S. Shamshirband, D. Petković, N. T. Pavlović, S. Ch, T. A. Altameem, and A. Gani, “Support vector machine firefly algorithm based optimization of lens system,” Appl. Opt. 54(1), 37–45 (2015).

[Crossref]

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput. 9(8), 1735–1780 (1997).

[Crossref]

S. Shamshirband, D. Petković, N. T. Pavlović, S. Ch, T. A. Altameem, and A. Gani, “Support vector machine firefly algorithm based optimization of lens system,” Appl. Opt. 54(1), 37–45 (2015).

[Crossref]

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

W. J. Smith, Modern Lens Design (McGraw Hill Professional, 2004).

D. Sturlesi and D. C. O’Shea, “Future of global optimization in optical design,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 54–69

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, (Curran Associates, Inc., 2012), pp. 1097–1105.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” in Advances in Neural Information Processing Systems 27, (Curran Associates, Inc., 2014), pp. 3104–3112.

G. Côté, J.-F. Lalonde, and S. Thibault, “Toward Training a Deep Neural Network to Optimize Lens Designs,” in Frontiers in Optics / Laser Science, (Optical Society of America, 2018), p. JW4A.28.

S. Thibault, C. Gagné, J. Beaulieu, and M. Parizeau, “Evolutionary algorithms applied to lens design: case study and analysis,” in Optical Design and Engineering II, vol. 5962 (International Society for Optics and Photonics, 2005).

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-Normalizing Neural Networks,” in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds. (Curran Associates, Inc., 2017) pp. 971–980.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” in Advances in Neural Information Processing Systems 27, (Curran Associates, Inc., 2014), pp. 3104–3112.

S. W. Weller, “Neural network optimization, components, and design selection,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 371–379.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

K. Höschel and V. Lakshminarayanan, “Genetic algorithms for lens design: a review,” J. Opt. 2, 463–470 (2018).

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015)..

[Crossref]

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput. 9(8), 1735–1780 (1997).

[Crossref]

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. Badrul Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).

[Crossref]

M. Isshiki, H. Ono, K. Hiraga, J. Ishikawa, and S. Nakadate, “Lens Design: Global Optimization with Escape Function,” Opt. Rev. 2(6), 463–470 (1995).

[Crossref]

S. Thibault, C. Gagné, J. Beaulieu, and M. Parizeau, “Evolutionary algorithms applied to lens design: case study and analysis,” in Optical Design and Engineering II, vol. 5962 (International Society for Optics and Photonics, 2005).

G. W. Forbes and A. E. W. Jones, “Towards global optimization with adaptive simulated annealing,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 144–154.

C. Menke, “Application of particle swarm optimization to the automatic design of optical systems,” in Optical Design and Engineering VII, vol. 10690 (International Society for Optics and Photonics, 2018).

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics Speech and Signal Processing (2013), pp. 6645–6649.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” in Advances in Neural Information Processing Systems 27, (Curran Associates, Inc., 2014), pp. 3104–3112.

S. W. Weller, “Neural network optimization, components, and design selection,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 371–379.

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The Handbook of Brain Theory and Neural Networks (MIT Press, 1995), pp. 255–258.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, (Curran Associates, Inc., 2012), pp. 1097–1105.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT University, 2016).

Zemax Development Corp., “Zebase 6 Optical Design Collection,” (2007).

G. Côté, J.-F. Lalonde, and S. Thibault, “Toward Training a Deep Neural Network to Optimize Lens Designs,” in Frontiers in Optics / Laser Science, (Optical Society of America, 2018), p. JW4A.28.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), (2017).

W. J. Smith, Modern Lens Design (McGraw Hill Professional, 2004).

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-Normalizing Neural Networks,” in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds. (Curran Associates, Inc., 2017) pp. 971–980.

Schott Corporation, “Optical Glass Catalog,” (2017).

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in 3rd International Conference on Learning Representations (ICLR 2015), (2015).

D. Sturlesi and D. C. O’Shea, “Future of global optimization in optical design,” in 1990 International Lens Design Conference, vol. 1354 (International Society for Optics and Photonics, 1991), pp. 54–69