Added links to projects.

Author: janfrancu

docs/src/projects.md

@@ -41,6 +41,12 @@ algorithms, which can work on discrete set of expressions. The goal is to
 write mutation and recombination - the basic operators of a genetic algorithm,
 but applied on top of Julia AST.
 
+Data: AI Feyman [database](https://space.mit.edu/home/tegmark/aifeynman.html) on symbolic regression (from [article](https://arxiv.org/pdf/1905.11481.pdf)/[code](https://github.com/SJ001/AI-Feynman))
+Inspiration: 
+- Logic Guided Genetic Algorithms [article](https://arxiv.org/pdf/2010.11328.pdf)/[code](https://github.com/DhananjayAshok/LGGA)
+- AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity [article](https://arxiv.org/pdf/2006.10782)
+- Genetic Programming for Julia: fast performance and parallel island model implementation [report](http://courses.csail.mit.edu/18.337/2015/projects/MorganFrank/projectReport.pdf)
+
 ## Distributed Optimization Package
 
 One click distributed optimization is at the heart of other machine learning 
@@ -49,6 +55,9 @@ missing in the Julia's Flux ecosystem. The goal of this project is to explore,
 implement and compare at least two state-of-the-art methods of distributed 
 gradient descent on data that will be provided for you.
 
+Some of the work has already been done in this area by one of our former students, 
+see [link](https://dspace.cvut.cz/handle/10467/97057).
+
 ## A Rule Learning Algorithm
 
 [Rule-based models](https://christophm.github.io/interpretable-ml-book/rules.html)