Add of some references and Summary

paper.bib

@@ -1,77 +1,65 @@
-@inproceedings{kajiya1986rendering,
-  title={The rendering equation},
-  author={Kajiya, James T},
-  booktitle={Proceedings of the 13th annual conference on Computer graphics and interactive techniques},
-  pages={143--150},
-  year={1986}
+@incollection{DBLP:books/sp/03/LourencoMS03,
+  author    = {Helena R. Louren{\c{c}}o and
+               Olivier C. Martin and
+               Thomas St{\"{u}}tzle},
+  editor    = {Fred W. Glover and
+               Gary A. Kochenberger},
+  title     = {Iterated Local Search},
+  booktitle = {Handbook of Metaheuristics},
+  series    = {International Series in Operations Research {\&} Management Science},
+  volume    = {57},
+  pages     = {320--353},
+  publisher = {Kluwer / Springer},
+  year      = {2003},
+  url       = {https://doi.org/10.1007/0-306-48056-5\_11},
+  doi       = {10.1007/0-306-48056-5\_11},
+  timestamp = {Mon, 15 Jun 2020 16:48:23 +0200},
+  biburl    = {https://dblp.org/rec/books/sp/03/LourencoMS03.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
 }
 
-@incollection{kollig2002efficient,
-  title={Efficient bidirectional path tracing by randomized quasi-Monte Carlo integration},
-  author={Kollig, Thomas and Keller, Alexander},
-  booktitle={Monte Carlo and Quasi-Monte Carlo Methods 2000},
-  pages={290--305},
-  year={2002},
-  publisher={Springer},
-  doi={10.1007/978-3-642-56046-0_19}
+@inproceedings{DBLP:conf/icmla/ChenJ07,
+  author    = {Xue{-}wen Chen and
+               Jong Cheol Jeong},
+  editor    = {M. Arif Wani and
+               Mehmed M. Kantardzic and
+               Tao Li and
+               Ying Liu and
+               Lukasz A. Kurgan and
+               Jieping Ye and
+               Mitsunori Ogihara and
+               Seref Sagiroglu and
+               Xue{-}wen Chen and
+               Leif E. Peterson and
+               Khalid Hafeez},
+  title     = {Enhanced recursive feature elimination},
+  booktitle = {The Sixth International Conference on Machine Learning and Applications,
+               {ICMLA} 2007, Cincinnati, Ohio, USA, 13-15 December 2007},
+  pages     = {429--435},
+  publisher = {{IEEE} Computer Society},
+  year      = {2007},
+  url       = {https://doi.org/10.1109/ICMLA.2007.35},
+  doi       = {10.1109/ICMLA.2007.35},
+  timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
+  biburl    = {https://dblp.org/rec/conf/icmla/ChenJ07.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
 }
 
-@article{delbracio2014boosting,
-  title={Boosting monte carlo rendering by ray histogram fusion},
-  author={Delbracio, Mauricio and Mus{\'e}, Pablo and Buades, Antoni and Chauvier, Julien and Phelps, Nicholas and Morel, Jean-Michel},
-  journal={ACM Transactions on Graphics (TOG)},
-  volume={33},
-  number={1},
-  pages={1--15},
-  year={2014},
-  publisher={ACM New York, NY, USA},
-  doi={10.1145/2532708}
-}
-
-@inproceedings{boughida2017bayesian,
-  title={Bayesian collaborative denoising for Monte Carlo rendering},
-  author={Boughida, Malik and Boubekeur, Tamy},
-  booktitle={Computer Graphics Forum},
-  volume={36},
-  number={4},
-  pages={137--153},
-  year={2017},
-  organization={Wiley Online Library},
-  doi={10.1111/cgf.13231}
-}
-
-@book{pharr2016physically,
-  title={Physically based rendering: From theory to implementation},
-  author={Pharr, Matt and Jakob, Wenzel and Humphreys, Greg},
-  year={2016},
-  publisher={Morgan Kaufmann}
-}
-
-@inproceedings{xie2012image,
-  title={Image denoising and inpainting with deep neural networks},
-  author={Xie, Junyuan and Xu, Linli and Chen, Enhong},
-  booktitle={Advances in neural information processing systems},
-  pages={341--349},
-  year={2012}
-}
-
-@article{chaitanya2017interactive,
-  title={Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder},
-  author={Chaitanya, Chakravarty R Alla and Kaplanyan, Anton S and Schied, Christoph and Salvi, Marco and Lefohn, Aaron and Nowrouzezahrai, Derek and Aila, Timo},
-  journal={ACM Transactions on Graphics (TOG)},
-  volume={36},
-  number={4},
-  pages={1--12},
-  year={2017},
-  publisher={ACM New York, NY, USA},
-  doi={10.1145/3072959.3073601}
-}
-
-@misc{pbrtp3d,
-  author = {​PrISE-3D},
-  title = {PrISE-3D customized pbrt-v3},
-  year = {2020},
-  publisher = {​GitHub},
-  journal = {​GitHub repository},
-  url = {https://github.com/prise-3d/pbrt-v3}
-}
+@article{DBLP:journals/remotesensing/PullanagariKY18,
+  author    = {Rajasheker R. Pullanagari and
+               Gabor Kereszturi and
+               Ian Yule},
+  title     = {Integrating Airborne Hyperspectral, Topographic, and Soil Data for
+               Estimating Pasture Quality Using Recursive Feature Elimination with
+               Random Forest Regression},
+  journal   = {Remote. Sens.},
+  volume    = {10},
+  number    = {7},
+  pages     = {1117},
+  year      = {2018},
+  url       = {https://doi.org/10.3390/rs10071117},
+  doi       = {10.3390/rs10071117},
+  timestamp = {Mon, 15 Jun 2020 16:51:53 +0200},
+  biburl    = {https://dblp.org/rec/journals/remotesensing/PullanagariKY18.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}

paper.md

@@ -22,10 +22,15 @@ bibliography: paper.bib
 
 # Summary
 
-`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+`Macop` for `Minimalist And Customizable Optimization Package` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+
+An underlying objective is to enable this package to be used in educational contexts as well. Allowing students to quickly develop their own algorithms.
 
 # Motivation
 
+During thesis work, the search for a solution with complex evaluation was necessary. The assessment in question consisted of evaluating a model fited with selected subset of available features from a features set. The solution was therefore the new model obtained and its fitness, the score obtained on this test basis.
+
+Exploring all the solutions was not feasible given the large amount of exploration space available. Otherwise it would have been preferable to use a Recursive Features Elimination method [@DBLP:journals/remotesensing/PullanagariKY18, @DBLP:conf/icmla/ChenJ07].
 
 # Application