Merge pull request #30 from sferes2/waf_debug_release

Updated waf (many changes, may break some old experiments) + many improvements to the waf system.

.gitignore

@@ -0,0 +1,11 @@
+exp/
+.*
+build/
+modules/
+\#*#
+*.par
+*.orig
+tests/serialize_g.xml
+modules.conf
+*.pyc
+buildoptions.log

.travis.yml

@@ -1,25 +1,26 @@
 language: cpp
+
+os:
+  - linux
+
+sudo: required
+
+dist: trusty
+
 compiler:
   - gcc
   - clang
 addons:
   apt:
     packages:
-    - libboost-dev
-    - libboost-test-dev 
-    - libboost-graph-dev 
-    - libboost-iostreams-dev
-    - libboost-math-dev
-    - libboost-program-options-dev
-    - libboost-serialization-dev
-    - libboost-system-dev 
-    - libboost-thread-dev
+    - libboost1.55-all-dev
     - libeigen3-dev
     - libtbb-dev
 
-install: 
+install:
 
 
 # Change this to your needs
 script:
-  dpkg -L libboost-serialization-dev && ./waf configure && ./waf -v
+  - ./waf configure
+  - ./waf --tests=yes -v

README.md

@@ -61,6 +61,7 @@ Peer-reviewed academic papers that used Sferes2:
 
 (you can find a pdf for most of these publications on http://scholar.google.com).
 ### 2015
+- Maestre, Carlos, Antoine Cully, Christophe Gonzales, and Stephane Doncieux. "Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach." In IEEE International Conference on Developmental and Learning and on Epigenetic Robotics. 2015.
 - Cully, Antoine, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. "Robots that can adapt like animals." Nature 521, no. 7553 (2015): 503-507.
 - Viejo, Guillaume, Mehdi Khamassi, Andrea Brovelli, and Benoît Girard. "Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning." Frontiers in behavioral neuroscience 9 (2015).
 - Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Innovation engines: Automated creativity and improved stochastic optimization via deep learning." In Proceedings of the Genetic and Evolutionary Computation Conference. 2015.

examples/ex_ea.cpp

@@ -30,10 +30,10 @@ struct Params {
     // size of the population
     SFERES_CONST unsigned size = 200;
     // number of generations
-    SFERES_CONST unsigned nb_gen = 2000;
+    SFERES_CONST unsigned nb_gen = 200000;
     // how often should the result file be written (here, each 5
     // generation)
-    SFERES_CONST int dump_period = 5;
+    SFERES_CONST int dump_period = 5000;
     // how many individuals should be created during the random
     // generation process?
     SFERES_CONST int initial_aleat = 1;
@@ -90,7 +90,9 @@ int main(int argc, char **argv) {
   // fitness, Pareto front, ...). Since they can also stores the best
   // individuals, they are the container of our results. We can add as
   // many statistics as required thanks to the boost::fusion::vector.
-  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>, stat::MeanFit<Params> >  stat_t;
+  // we need sferes:: because of a name clash with the C function stat()...
+  typedef boost::fusion::vector<stat::BestFit<phen_t, Params>,
+          stat::MeanFit<Params> >  stat_t;
   // Modifiers are functors that are run once all individuals have
   // been evalutated. Their typical use is to add some evolutionary
   // pressures towards diversity (e.g. fitness sharing). Here we don't