Why evolutionary robotics will matter

Studies in Computational Intelligence

Volumn 341, Issue , 2011, Pages 37-41

  Stanley, Kenneth O ^a  

^a University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79952120640     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-18272-3_3     Document Type: Conference Paper

References (24)

1. Allen, B., Faloutsos, P.: Complex networks of simple neurons for bipedal locomotion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2009)
2. Baxter, J.: The evolution of learning algorithms for artificial neural networks. In: Green, D., Bossomaier, T. (eds.) Complex Systems, pp. 313-326. IOS Press, Amsterdam (1992)
3. Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evolutionary Intelligence 1, 47-62 (2008)
4. Floreano, D., Urzelai, J.: Evolutionary robots with online self-organization and behavioral fitness. Neural Networks 13, 431-443 (2000) (Pubitemid 30447428)
5. Gauci, J., Stanley, K.O.: Autonomous evolution of topographic regularities in artificial neural networks. Neural Computation (to appear, 2010)
6. Hein, D., Hild, M., Berger, R.: Evolution of biped walking using neural oscillators and physical simulation. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.) RoboCup 2007: Robot Soccer World Cup XI. LNCS (LNAI), vol. 5001, pp. 433-440. Springer, Heidelberg (2008)
7. Khatib, O.: Introduction to robotics (CS223A) syllabus. Stanford University (2007)
8. Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Bullock, S., Noble, J., Watson, R., Bedau, M. (eds.) Proceedings of the Eleventh International Conference on Artificial Life (Alife XI). MIT Press, Cambridge (2008)
9. McHale, G., Husbands, P.: Gasnets and other evolvable neural networks applied to bipedal locomotion. In: From Animals to Animats 8 (2004)
10. Mouret, J.-B.: Novelty-based multiobjectivization. In: Proceedings of the Workshop on Exploring New Horizons in Evolutionary Design of Robots IEEE/RSJ International Conference on Intelligent Robots and Systems (2009)
11. Niv, Y., Joel, D., Meilijson, I., Ruppin, E.: Evolution of reinforcement learning in uncertain environments: A simple explanation for complex foraging behaviors. Adaptive Behavior 10(1), 5-24 (2002) (Pubitemid 36425178)
12. Nolfi, S., Floreano, D.: Evolutionary Robotics. MIT Press, Cambridge (2000)
13. Nolfi, S., Parisi, D.: Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior 5, 75-98 (1996)
14. Reil, T., Husbands, P.: Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Transactions on Evolutionary Computation 6(2), 159-168 (2002) (Pubitemid 34555370)
15. Risi, S., Stanley, K.O.: Indirectly encoding neural plasticity as a pattern of local rules. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., Mouret, J.-B. (eds.) SAB 2010. LNCS, vol. 6226, pp. 533-543. Springer, Heidelberg (2010)
16. Risi, S., Vanderbleek, S.D., Hughes, C.E., Stanley, K.O.: How novelty search escapes the deceptive trap of learning to learn. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009. ACM Press, New York (2009)
17. Soltoggio, A.: Neural Plasticity and Minimal Topologies for Reward-Based Learning. In: Proceedings of the 8th International Conference on Hybrid Intelligent Systems, pp. 637-642. IEEE Computer Society, Los Alamitos (2008)
18. Soltoggio, A., Bullinaria, J.A., Mattiussi, C., Dürr, P., Floreano, D.: Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios. In: Artificial Life XI, pp. 569-576. MIT Press, Cambridge (2008)
19. Soltoggio, A., Dürr, P., Mattiussi, C., Floreano, D.: Evolving neuromodulatory topologies for reinforcement learning-like problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (2007)
20. Stanley, K.O., Bryant, B.D., Miikkulainen, R.: Evolving adaptive neural networks with and without adaptive synapses. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, CEC 2003. IEEE Press, Canberra (2003)
21. Stanley, K.O., D'Ambrosio, D.B., Gauci, J.: A hypercube-based indirect encoding for evolving large-scale neural networks. Artificial Life 15(2), 185-212 (2009)
22. Stanley, K.O.,Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10, 99-127 (2002)
23. Van de Panne, M., Lamouret, A.: Guided optimization for balanced locomotion. In: Terzopoulos, D., Thalmann, D. (eds.) Sixth Eurographics Workshop on Animation and Simulation, pp. 165-177. Springer, Heidelberg (1995)
24. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423-1447 (1999) (Pubitemid 30503424)