diff --git a/README.md b/README.md
index 7ca479f7b..cb9e0e047 100644
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+++ b/README.md
@@ -4,6 +4,7 @@ DEAP is a novel evolutionary computation framework for rapid prototyping and tes
 ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and [SCOOP](http://scoop.googlecode.com).
 
 DEAP includes the following features:
+
   * Genetic algorithm using any imaginable representation
     * List, Array, Set, Dictionary, Tree, Numpy Array, etc.
   * Genetic programing using prefix trees
@@ -116,6 +117,7 @@ Authors of scientific papers including results generated using DEAP are encourag
   * François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné, "DEAP: A Python Framework for Evolutionary Algorithms", in !EvoSoft Workshop, Companion proc. of the Genetic and Evolutionary Computation Conference (GECCO 2012), July 07-11 2012. [Paper](http://goo.gl/pXXug)
 
 ## Projects using DEAP
+
   * Lara-Cabrera, R., Cotta, C. and Fernández-Leiva, A.J. (2014). Geometrical vs topological measures for the evolution of aesthetic maps in a rts game, Entertainment Computing,
   * Macret, M. and Pasquier, P. (2013). Automatic Tuning of the OP-1 Synthesizer Using a Multi-objective Genetic Algorithm. In Proceedings of the 10th Sound and Music Computing Conference (SMC). (pp 614-621).
   * Fortin, F. A., Grenier, S., & Parizeau, M. (2013, July). Generalizing the improved run-time complexity algorithm for non-dominated sorting. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (pp. 615-622). ACM.