[Albiez, J. and Berns, K. (2004). Biological inspired walking—How much nature do we need?, in M. A. Armada and P. de González Santos (Eds), Climbing and Walking Robots. Proceedings of the 7th International Conference CLAWAR 2004, Springer, Berlin, pp. 357-364.]Search in Google Scholar
[Annunziato, M. and Pizzuti, S. (2000). Adaptive parameterization of evolutionary algorithms driven by reproduction and competition, Proceedings of the European Symposium on Intelligent Techniques (ESIT 2000), Aachen, Germany, pp. 31-35.]Search in Google Scholar
[Arabas, J. (2001). Lectures on Evolutionary Algorithms, WNT, Warsaw, (in Polish).]Search in Google Scholar
[Bäck, T., Hoffmeister, F. and H.-P. Schwefel (1991). A survey of evolution strategies, in R. K. Belew and L. B. Booker (Eds), Proceedings of the 4th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, CA, pp. 2-9.]Search in Google Scholar
[Barfoot, T. D., Earon, E. J. P. and D'Eleuterio, G. M. T. (2006). Experiments in learning distributed control for a hexapod robot, Robotics and Autonomous Systems 54(10): 864-872.10.1016/j.robot.2006.04.009]Search in Google Scholar
[Beer, R. D., Quinn, R. D., Chiel, H. J. and Ritzmann, R. E. (1997). Biologically inspired approaches to robotics: What can we learn from insects?, Communications of the ACM 40(3): 31-38.10.1145/245108.245118]Search in Google Scholar
[Belter, D., Kasiński, A. and Skrzypczyński, P. (2008). Evolving feasible gaits for a hexapod robot by reducing the space of possible solutions, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 2673-2678.]Search in Google Scholar
[Belter, D. and Skrzypczyński, P. (2009). Population based methods for identification and optimization of a walking robot model, in K. Kozlowski (Ed.), Robot Motion and Control 2009, Lecture Notes in Control and Information Sciences, Vol. 396, Springer, Berlin, pp. 185-195.10.1007/978-1-84882-985-5_18]Search in Google Scholar
[Busch, J., Ziegler, J., Aue, C., Ross, A., Sawitzki, D. and Banzhaf, W. (2002). Automatic generation of control programs for walking robots using genetic programming, in J. Foster, E. Lutton, J. Miller, C. Ryan and A. Tettamanzi (Eds), Genetic Programming, Proceedings of the 5th European Conference EuroGP 2002, Lecture Notes in Computer Science, Vol. 2278, Springer, Berlin, pp. 258-267.10.1007/3-540-45984-7_25]Search in Google Scholar
[Chernova, S. and Veloso, M. (2004). An evolutionary approach to gait learning for four-legged robots, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, New Orleans, LA, USA, pp. 2562-2567.]Search in Google Scholar
[Dorigo, M. and Colombetti, M. (1997). Robot Shaping: An Experiment in Behavior Engineering, MIT Press, Cambridge, MA.10.7551/mitpress/5988.001.0001]Search in Google Scholar
[Figliolini, G., Stan, S.-D. and Rea, P. (2007). Motion analysis of the leg tip of a six-legged walking robot, Proceedings of the 12th IFToMM World Congress, Besançon, France, (on CD-ROM).]Search in Google Scholar
[Fukuoka, Y., Kimura, H. and Cohen, A. H. (2003). Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts, International Journal on Robotics Research 22(4): 187-202.10.1177/0278364903022003004]Search in Google Scholar
[Gallagher, J., Beer, D. R., Espenschied, K. and Quinn, R. D. (1996). Application of evolved locomotion controllers to a hexapod robot, Robotics and Autonomous Systems 19(1): 95-103.10.1016/S0921-8890(96)00036-X]Search in Google Scholar
[Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.]Search in Google Scholar
[Holland, J. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.]Search in Google Scholar
[Hornby, G., Takamura, S., Yamamoto, T. and Fujita, M. (2005). Autonomous evolution of dynamic gaits with two quadruped robots, IEEE Transactions on Robotics 21(3): 402-410.10.1109/TRO.2004.839222]Search in Google Scholar
[Huber, M. and Grupen, R. A. (1997). A feedback control structure for on-line learning tasks, Robotics and Autonomous Systems 22(3-4): 303-315.10.1016/S0921-8890(97)00044-4]Search in Google Scholar
[Jakobi, N. (1998). Running across the reality gap: Octopod locomotion evolved in a minimal simulation, in P. Husbands and J.-A. Meyer (Eds), Evolutionary Robotics. Proceedings of the First European Workshop EvoRobot98, Lecture Notes in Computer Science, Vol. 1468, Springer, Berlin, pp. 39-58.10.1007/3-540-64957-3_63]Search in Google Scholar
[Jakobi, N., Husbands, P. and Harvey, I. (1995). Noise and the reality gap: The use of simulation in evolutionary robotics, Proceedings of the 3rd European Conference on Articial Life (ECAL'95), Granada, Spain, pp. 704-720.]Search in Google Scholar
[Kimura, H., Yamashita, T. and Kobayashi, S. (2001). Reinforcement learning of walking behavior for a four-legged robot, Proceedings of the IEEE Conference on Decisions and Control, Orlando, FL, USA, pp. 411-416.]Search in Google Scholar
[Kirchner, F. (1998). Q-learning of complex behaviours on a six-legged walking machine, Robotics and Autonomous Systems 25(3-4): 256-263.10.1016/S0921-8890(98)00054-2]Search in Google Scholar
[Kowalczuk, Z. and Białaszewski, T. (2006). Niching mechanisms in evolutionary computations, International Journal of Applied Mathematics and Computer Science 16(1): 59-84.]Search in Google Scholar
[Kozlowski, K. (1998). Modelling and Identification in Robotics, Springer, Berlin.10.1007/978-1-4471-0429-2]Search in Google Scholar
[Kumar, V. R. and Waldron, K. J. (1989). Adaptive gait control for a walking robot, Journal of Robotic Systems 6(1): 49-76.10.1002/rob.4620060105]Search in Google Scholar
[Lewis, M., Fagg, A. and Bekey, G. (1994). Genetic algorithms for gait synthesis in a hexapod robot, in Y. Zheng (Ed.), Recent Trends in Mobile Robots, World Scientific, Singapore, pp. 317-331.10.1142/9789814354301_0011]Search in Google Scholar
[Luk, B. L., Galt, S. and Chen, S. (2001). Using genetic algorithms to establish efficient walking gaits for an eight-legged robot, International Journal of Systems Science 32(6): 703-713.10.1080/00207720117230]Search in Google Scholar
[Maes, P. and Brooks, R. A. (1990). Learning to coordinate behaviors, Proceedings of the 8th National Conference on Artificial Intelligence (AAAI 1990), Boston, MA, USA, pp. 796-802.]Search in Google Scholar
[Mataric, M. and Cliff, D. (1996). Challenges in evolving controllers for physical robots, Robotics and Autonomous Systems 19(1): 67-83.10.1016/S0921-8890(96)00034-6]Search in Google Scholar
[Parker, G. B. and Mills, J.W. (1999). Adaptive hexapod gait control using anytime learning with fitness biasing, Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, FL, USA, pp. 519-524.]Search in Google Scholar
[Perry, M. J., Koh, C. G. and Choo, Y. S. (2006). Modified genetic algorithm strategy for structural identification, Automatica 84(8-9): 529-540.10.1016/j.compstruc.2005.11.008]Search in Google Scholar
[Ridderström, C. (1999). Legged locomotion control—A literature survey, Technical Report TRITA-MMK 1999:27, Royal Institute of Technology, Stockholm.]Search in Google Scholar
[Ritzmann, R. E., Quinn, R. D. and Fischer, M. C. (2004). Convergent evolution and locomotion through complex terrain by insects, vertebrates and robots, Arthropod Structure & Development 33(3): 361-379.10.1016/j.asd.2004.05.001]Search in Google Scholar
[Skrzypczyński, P. (2004a). Experimental validation of the fuzzy reactive behaviours evolved in simulation, in F. Groen, N. Amato, A. Bonarini, E. Yoshida and B. Kröse (Eds), Intelligent Autonomous Systems 8, IOS Press, Amsterdam, pp. 464-471.]Search in Google Scholar
[Skrzypczyński, P. (2004b). Shaping in a realistic simulation: An approach to learn reactive fuzzy rules, Preprints of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal, (on CD-ROM).10.1016/S1474-6670(17)32083-9]Search in Google Scholar
[Smith, R. (2007). Open dynamics engine http://www.ode.org]Search in Google Scholar
[Song, S.-M. and Waldron, K. J. (1989). Machines that Walk: The Adaptive Suspension Vehicle, MIT Press, Cambridge, MA.]Search in Google Scholar
[Svinin, M. M., Yamada, K. and Ueda, K. (2001). Emergent synthesis of motion patterns for locomotion robots, Artificial Intelligence in Engineering 15(4): 353-363.10.1016/S0954-1810(01)00027-9]Search in Google Scholar
[Tuyls, K., Maes, S. and Manderick, B. (2003). Reinforcement learning in large state spaces: Simulated robotic soccer as a testbed, RoboCup 2002: Robot Soccer World Cup VI, Lecture Notes in Computer Science, Vol. 2752, Springer, Berlin, pp. 319-326.]Search in Google Scholar
[Walas, K., Belter, D. and Kasiński, A. (2008). Control and environment sensing system for a six-legged robot, Journal of Automation, Mobile Robotics and Intelligent Systems 2(3): 26-31.]Search in Google Scholar
[Walker, J., Garrett, S. and Wilson, M. (2003). Evolving controllers for real robots: A survey of the literature, Adaptive Behavior 11(3): 179-203.10.1177/1059712303113003]Search in Google Scholar
[Wilson, D. M. (1966). Insect walking, Annaul Reiew of Entomology 11(1): 103-122.10.1146/annurev.en.11.010166.000535]Search in Google Scholar
[Yang, J.-M. (2009). Fault-tolerant gait planning for a hexapod robot walking over rough terrain, Journal of Intelligent and Robotic Systems 54(4): 613-627.10.1007/s10846-008-9282-x]Search in Google Scholar
[Zagal, J. C., Ruiz-del-Solar, J. and Vallejos, P. (2004). Back to reality: Crossing the reality gap in evolutionary robotics, Preprints of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal, (on CD-ROM).10.1016/S1474-6670(17)32084-0]Search in Google Scholar