sferes2

Sferes2 is a high-performance, multi-core, lightweight, generic C++98 framework for evolutionary computation. It is intently kept small to stay reliable and understandable.

Sferes2 heavily relies on template-based meta-programming in C++ to get both abstraction and execution speed.

If you use this software in an academic article, please cite:

Mouret, J.-B. and Doncieux, S. (2010). SFERESv2: Evolvin' in the Multi-Core World. Proc. of Congress on Evolutionary Computation (CEC) Pages 4079--4086.

The article is available here: http://www.isir.upmc.fr/files/2010ACTI1524.pdf

@INPROCEEDINGS{Mouret2010,
	AUTHOR = {Mouret, J.-B. and Doncieux, S.},
	TITLE = {{SFERES}v2: Evolvin' in the Multi-Core World},
	YEAR = {2010},
	BOOKTITLE = {Proc. of Congress on Evolutionary Computation (CEC)},
	PAGES = {4079--4086}
}

Documentation (including instruction for compilation)

We are in the process of porting the documentation to http://sferes2.github.io/sferes2/ (the old documentation is on the wiki here: https://github.com/jbmouret/sferes2/wiki ). You will find tutorials, installation instructions etc.

Warning Sferes2 now requires a C++11 compiler (recent versions of g++ or clang++ work fine).

Main optional modules

• evolvable neural networks: https://github.com/sferes2/nn2
• khepera-like simulator: https://github.com/sferes2/fastsim
• Map-Elites (we will soon integrate a more flexible version in the master branch of sferes_v2): https://github.com/sferes2/map_elites

Design

The following choices were made in the initial design:

• use of modern c++ techniques (template-based programming) to employ object-oriented programming without the cost of virtual functions;
• use of Intel TBB to take full advantages of multicore and SMP systems;
• use of boost libraries when it's useful (shared_ptr, serialization, filesystem, test,...);
• use of MPI to distribute the computational cost on clusters;
• a full set of unit tests;
• no configuration file: a fully optimized executable is built for each particular experiment.

Sferes2 is extended via modules and experiments.

Sferes2 should work on most Unix systems (in particular, GNU/Linux and OSX). It successfully compiles with gcc, clang and icc (if it is not the case, please file a bug report in the issue tracker).

Author

• Jean-Baptiste Mouret jean-baptiste.mouret@inria.fr: main author and maintainer

Other contributors

• Stephane Doncieux doncieux@isir.upmc.fr
• Konstantinos Chatzilygeroudis konstantinos.chatzilygeroudis@inria.fr
• Paul Tonelli tonelli@isir.upmc.fr (documentation)
• Many members of ISIR (http://isir.upmc.fr)

Peer-reviewed academic papers that used Sferes2:

If you used Sferes2 in an academic paper, please send us an e-mail (jean-baptiste.mouret@inria.fr) so that we can add it here!

(you can find a pdf for most of these publications on http://scholar.google.com).

2019

1. Kaushik R, Chatzilygeroudis K, Mouret JB. Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards.Proceedings of CoRL (Conference on Robot Learning), 2019
2. Ellefsen KO, Huizinga J, Torresen J. Guiding Neuroevolution with Structural Objectives. Evolutionary computation. 2019 Feb 15:1-26.
3. Nordmoen J, Samuelsen E, Ellefsen KO, Glette K. Dynamic mutation in MAP-Elites for robotic repertoire generation. In Artificial Life Conference Proceedings 2018 Jul (pp. 598-605).

2018

1. Nygaard TF, Martin CP, Samuelsen E, Torresen J, Glette K. Real-world evolution adapts robot morphology and control to hardware limitations. InProceedings of the Genetic and Evolutionary Computation Conference 2018 Jul 2 (pp. 125-132). ACM.
2. Pautrat R, Chatzilygeroudis K, Mouret JB. Bayesian optimization with automatic prior selection for data-efficient direct policy search. In2018 IEEE International Conference on Robotics and Automation (ICRA) 2018 May 21 (pp. 7571-7578). IEEE.
3. Nordmoen J, Ellefsen KO, Glette K. Combining map-elites and incremental evolution to generate gaits for a mammalian quadruped robot. In International Conference on the Applications of Evolutionary Computation 2018 Apr 3 (pp. 719-733). Springer, Cham.
4. Vassiliades V, Mouret JB. Discovering the elite hypervolume by leveraging interspecies correlation. InProceedings of the Genetic and Evolutionary Computation Conference 2018 Jul 2 (pp. 149-156). ACM.

2017

1. Maurice, P., Padois, V., Measson, Y., & Bidaud, P. (2017). Human-oriented design of collaborative robots. International Journal of Industrial Ergonomics, 57, 88-102.
2. Viejo, G., Girard, B., Procyk, E., & Khamassi, M. (2017). Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task. Behavioural Brain Research.
3. Vassiliades, V., Chatzilygeroudis, K., & Mouret, J. B. (2017). Using centroidal voronoi tessellations to scale up the multi-dimensional archive of phenotypic elites algorithm. IEEE Transactions on Evolutionary Computation.
4. Pontes J, Doncieux S, Santos C, Padois V. An adaptive approach to humanoid locomotion. InAdvances in Cooperative Robotics 2017 (pp. 437-444).
5. Ellefsen KO, Tørresen J. Evolving neural networks with multiple internal models. In Artificial Life Conference Proceedings 14 2017 Sep (pp. 138-145).
6. Cully A, Demiris Y. Quality and diversity optimization: A unifying modular framework. IEEE Transactions on Evolutionary Computation. 2017 Jun 26;22(2):245-59.

2016

1. Mengistu, H., Huizinga, J., Mouret, J.-B., & Clune, J. The evolutionary origins of hierarchy. PLoS Computational Biology, Public Library of Science, 2016, 12 (6),
2. Velez, R., and Clune, J.. "Identifying Core Functional Networks and Functional Modules within Artificial Neural Networks via Subsets Regression." Proceedings of the Genetic and Evolutionary Computation Conference. 2016.
3. Tarapore, D. Clune, J., Cully, A., and Mouret, J.-B "How Do Different Encodings Influence the Performance of the MAP-Elites Algorithm?" Proceedings of the Genetic and Evolutionary Computation Conference. 2016.
4. Huizinga J., Mouret J.-B., Clune J. "Does aligning phenotypic and genotypic modularity improve the evolution of neural networks?" Proceedings of the Genetic and Evolutionary Computation Conference. 2016.
5. Norouzzadeh M., Clune J. Neuromodulation improves the evolution of forward models. Proceedings of the Genetic and Evolutionary Computation Conference. 2016.
6. Stanton, C., and Clune J. "Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills throughout Their Lifetime." PloS one 11.9 (2016): e0162235.
7. Nguyen A, Yosinski J, Clune J. Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. Evolutionary Computation. 2016 Sep;24(3):545-72.
8. Bernard, A., André, J. B., & Bredeche, N. (2016). Evolving specialisation in a population of heterogeneous robots: the challenge of bootstrapping and maintaining genotypic polymorphism. Artificial Life, 15, 1-8.
9. Pontes, J., Doncieux, S., Santos, C., & Padois, V. (2016). An Adaptive Approach to Humanoid Locomotion. In Advances in Cooperative Robotics--Proceedings of the 19th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (pp. 437-444).
10. Velez R, Clune J. Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks. PloS one. 2017 Nov 16;12(11):e0187736.
11. Zimmer M, Doncieux S. Bootstrapping $ q $-learning for robotics from neuro-evolution results. IEEE Transactions on Cognitive and Developmental Systems. 2017 Mar 15;10(1):102-19.

2015

1. 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.
2. Cully, Antoine, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. "Robots that can adapt like animals." Nature 521, no. 7553 (2015): 503-507.
3. 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).
4. 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.
5. 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.
6. Shrouf, Fadi, Joaquin Ordieres-Meré, Alvaro García-Sánchez, and Miguel Ortega-Mier. "Optimizing the production scheduling of a single machine to minimize total energy consumption costs." Journal of Cleaner Production 67 (2014): 197-207.
7. Ellefsen, Kai Olav, Jean-Baptiste Mouret, and Jeff Clune. "Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills." PLoS Comput Biol 11.4 (2015): e1004128.
8. Cully, Antoine, and J-B. Mouret. "Evolving a Behavioral Repertoire for a Walking Robot." Evolutionary computation (2015).
9. Mouret, Jean-Baptiste, and Jeff Clune. "Illuminating search spaces by mapping elites." arXiv preprint arXiv:1504.04909 (2015).
10. Tarapore, Danesh, and Jean-Baptiste Mouret. "Evolvability signatures of generative encodings: beyond standard performance benchmarks." Information Sciences (2015).
11. Nguyen, A., Yosinski, J. and Clune, J.. "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images." Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015.
12. Bernard, A., André, J.B. and Bredeche, N., 2015. Evolution of Cooperation in Evolutionary Robotics: the Tradeoff between Evolvability and Efficiency. In Proceedings of the European Conference on Artificial Life 2015 (pp. 195-502).

2014

1. Doncieux, S. (2014). Knowledge Extraction from Learning Traces in Continuous Domains. AAAI 2014 fall Symposium ''Knowledge, Skill, and Behavior Transfer in Autonomous Robots''. Pages 1-8.
2. Lesaint, F., Sigaud, O., Clark, J. J., Flagel, S. B., & Khamassi, M. (2014). Experimental predictions drawn from a computational model of sign-trackers and goal-trackers. Journal of Physiology-Paris.
3. Lesaint, F., Sigaud, O., Flagel, S. B., Robinson, T. E., & Khamassi, M. (2014). Modelling Individual Differences in the Form of Pavlovian Conditioned Approach Responses: A Dual Learning Systems Approach with Factored Representations. PLoS computational biology, 10(2), e1003466.
4. Shrouf, F., Ordieres-Meré, J., García-Sánchez, A., & Ortega-Mier, M. (2014). Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 67, 197-207.
5. Huizinga, J., Mouret, J. B., & Clune, J. (2014). Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique. In Proceedings of GECCO (pp. 1-8).
6. Li, J., Storie, J., & Clune, J. (2014). Encouraging Creative Thinking in Robots Improves Their Ability to Solve Challenging Problems. Proceedings of GECCO (pp 1-8)
7. Tarapore, D. and Mouret, J.-B. (2014). Comparing the evolvability of generative encoding schemes. Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, MIT Press, publisher. Pages 1-8.

2013

1. Koos, S. and Cully, A. and Mouret, J.-B. (2013). Fast Damage Recovery in Robotics with the T-Resilience Algorithm. International Journal of Robotics Research. Vol 32 No 14 Pages 1700-1723.
2. Tonelli, P. and Mouret, J.-B. (2013). On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks. PLoS One. Vol 8 No 11 Pages e79138
3. Clune*, J. and Mouret, J.-B. and Lipson, H. (2013). The evolutionary origins of modularity. Proceedings of the Royal Society B. Vol 280 (J. Clune and J.-B. Mouret contributed equally to this work) Pages 20122863
4. Koos, S. and Mouret, J.-B. and Doncieux, S. (2013). The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics. IEEE Transactions on Evolutionary Computation. Vol 17 No 1 Pages 122 - 145
5. Doncieux, S. and Mouret, J.B. (2013). Behavioral Diversity with Multiple Behavioral Distances. Proc. of IEEE Congress on Evolutionary Computation, 2013 (CEC 2013). Pages 1-8
6. Cully, A. and Mouret, J.-B. (2013). Behavioral Repertoire Learning in Robotics. Genetic and Evolutionary Computation Conference (GECCO). Pages 175-182.
7. Doncieux, S. (2013). Transfer Learning for Direct Policy Search: A Reward Shaping Approach. Proceedings of ICDL-EpiRob conference. Pages 1-6.

2012

1. Mouret, J.-B. and Doncieux, S. (2012). Encouraging Behavioral Diversity in Evolutionary Robotics: an Empirical Study. Evolutionary Computation. Vol 20 No 1 Pages 91-133.
2. Ollion, Charles and Doncieux, Stéphane (2012). Towards Behavioral Consistency in Neuroevolution. From Animals to Animats: Proceedings of the 12th International Conference on Adaptive Behaviour (SAB 2012), Springer, publisher. Pages 1-10.
3. Ollion, C. and Pinville, T. and Doncieux, S. (2012). With a little help from selection pressures: evolution of memory in robot controllers. Proc. Alife XIII. Pages 1-8.

2011

1. Rubrecht, S. and Singla, E. and Padois, V. and Bidaud, P. and de Broissia, M. (2011). Evolutionary design of a robotic manipulator for a highly constrained environment. Studies in Computational Intelligence, New Horizons in Evolutionary Robotics, Springer, publisher. Vol 341 Pages 109-121.
2. Doncieux, S. and Hamdaoui, M. (2011). Evolutionary Algorithms to Analyse and Design a Controller for a Flapping Wings Aircraft. New Horizons in Evolutionary Robotics Extended Contributions from the 2009 EvoDeRob Workshop, Springer, publisher. Pages 67--83.
3. Mouret, J.-B. (2011). Novelty-based Multiobjectivization. New Horizons in Evolutionary Robotics: Extended Contributions from the 2009 EvoDeRob Workshop, Springer, publisher. Pages 139--154.
4. Pinville, T. and Koos, S. and Mouret, J-B. and Doncieux, S. (2011). How to Promote Generalisation in Evolutionary Robotics: the ProGAb Approach. GECCO'11: Proceedings of the 13th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 259--266.
5. Koos, S. and Mouret, J-B. (2011). Online Discovery of Locomotion Modes for Wheel-Legged Hybrid Robots: a Transferability-based Approach. Proceedings of CLAWAR, World Scientific Publishing Co., publisher. Pages 70-77.
6. Tonelli, P. and Mouret, J.-B. (2011). On the Relationships between Synaptic Plasticity and Generative Systems. Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. Pages 1531--1538. (Best paper of the Generative and Developmental Systems (GDS) track).
7. Terekhov, A.V. and Mouret, J.-B. and Grand, C. (2011). Stochastic optimization of a chain sliding mode controller for the mobile robot maneuvering. Proceedings of IEEE / IROS Int. Conf. on Robots and Intelligents Systems. Pages 4360 - 4365

2010

1. Mouret, J.-B. and Doncieux, S. and Girard, B. (2010). Importing the Computational Neuroscience Toolbox into Neuro-Evolution---Application to Basal Ganglia. GECCO'10: Proceedings of the 12th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 587--594.
2. Koos, S. and Mouret, J.-B. and Doncieux, S. (2010). Crossing the Reality Gap in Evolutionary Robotics by Promoting Transferable Controllers. GECCO'10: Proceedings of the 12th annual conference on Genetic and evolutionary computation ACM, publisher . Pages 119--126.
3. Doncieux, S. and Mouret, J.-B. (2010). Behavioral diversity measures for Evolutionary Robotics. WCCI 2010 IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computation (CEC). Pages 1303--1310.
4. Terekhov, A.V. and Mouret, J.-B. and Grand, C. (2010). Stochastic optimization of a neural network-based controller for aggressive maneuvers on loose surfaces. Proceedings of IEEE / IROS Int. Conf. on Robots and Intelligents Systems. Pages 4782 - 4787
5. Terekhov, A.V and Mouret, J.-B. and Grand, C (2010). Stochastic multi-objective optimization for aggressive maneuver trajectory planning on loose surface. Proceedings of IFAC: the 7th Symposium on Intelligent Autonomous Vehicles. Pages 1-6
6. Liénard, J. and Guillot, A. and Girard, B. (2010). Multi-Objective Evolutionary Algorithms to Investigate Neurocomputational Issues : The Case Study of Basal Ganglia Models. From animals to animats 11, Springer, publisher. Vol 6226 Pages 597--606

2009

1. Koos, S. and Mouret, J.-B. and Doncieux, S. (2009). Automatic system identification based on coevolution of models and tests. IEEE Congress on Evolutionary Computation, 2009 (CEC 2009). Pages 560--567
2. Mouret, J.-B. and Doncieux, S. (2009). Evolving modular neural-networks through exaptation. IEEE Congress on Evolutionary Computation, 2009 (CEC 2009). Pages 1570--1577. (Best student paper award)
3. Mouret, J.-B. and Doncieux, S. (2009). Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. IEEE Congress on Evolutionary Computation, 2009 (CEC 2009). Pages 1161 - 1168
4. Mouret, J.-B. and Doncieux, S. (2009). Using Behavioral Exploration Objectives to Solve Deceptive Problems in Neuro-evolution. GECCO'09: Proceedings of the 11th annual conference on Genetic and evolutionary computation , ACM, publisher. Pages 627--634.