Abstract
One advantage of the asynchronous and distributed character of embodied evolution is that it can be executed on real robots without external supervision. Further, evolutionary progress can be measured in real time instead of in generation based evaluation cycles. By combining embodied evolution with lifetime learning, we investigated a largely neglected aspect with respect to the common assumption that learning can guide evolution, the influence of maturation time during which an individual can develop its behavioral skills. Even though we found only minor differences between the evolution with and without learning, our results, derived from competitive evolution in predator-prey systems, demonstrate that the right timing of maturation is crucial for the progress of evolutionary success. Our findings imply that the time of maturation has to be considered more seriously as an important factor to build up empirical evidence for the hypothesis that learning facilitates evolution.
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References
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Harvey, I., Di Paolo, E.A., Wood, R., Quinn, M., Tuci, E.: Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11, 79–98 (2005)
Taylor, T.: Creativity in evolution: Individuals, interactions and environments. In: Creative Evolutionary Systems, pp. 79–108. Morgan Kaufmann, San Francisco (2001)
Bianco, R., Nolfi, S.: Toward open-ended evolutionary robotics: Evolving elementary robotic units able to self-assemble and self-reproduce. Connection Science 16, 227–248 (2004)
Nolfi, S., Floreano, D.: Learning and evolution. Autonomous Robots 7, 89–113 (1999)
Taylor, T., Massey, C.: Recent developments in the evolution of morphologies and controllers for physically simulated creatures. Artificial Life 7, 77–87 (2001)
Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39, 1–18 (2002)
Nolfi, S., Floreano, D.: Co-evolving predator and prey robots: Do ‘arm races’ arise in artificial evolution? Artificial Life 4, 311–335 (1998)
Hinton, G.E., Nowlan, S.J.: How learning guides evolution. Complex System 1, 495–502 (1987)
Urzelai, J., Floreano, D.: Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments. Evolutionary Computation 9, 495–524 (2001)
Werner, G., Dyer, D.: Evolution of communication in artificial organisms. In: Proc. Artificial Life II, Addison-Wesley, Reading (1991)
van Valen, L.: A new evolutionary law. Evolutionary Theory 1, 1–30 (1973)
Floreano, D., Nolfi, S.: God save the red queen! Competition in co-evolutionary robotics. In: Genetic Programming 1997: Proc. of the Second Annual Conference, pp. 398–406 (1997)
Porr, B., Wörgötter, F.: Strongly improved stability and faster convergence of temporal sequence learning by utilising input correlations only. Neural Computation 18, 1380–1412 (2006)
Hülse, M., Wischmann, S., Pasemann, F.: Structure and function of evolved neuro-controllers for autonomous robots. Connection Science 16, 249–266 (2004)
Buason, G., Bergfeldt, N., Ziemke, T.: Brains, bodies, and beyond: Competitive co-evolution of robot controllers, morphologies and environments. Genetic Programming and Evolvable Machines 6, 25–51 (2005)
Stamm, K.: Individual learning and the dynamics in predator-prey populations. Master’s thesis, University of Göttingen (2007)
Di Paolo, E.A.: Organismically-inspired robotics: Homeostatic adaptation and teleology beyond the closed sensorimotor loop. In: Dynamical Systems Approach to Embodiment and Sociality, pp. 19–42. Advanced Knowledge International, Magill (2003)
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Wischmann, S., Stamm, K., Wörgötter, F. (2007). Embodied Evolution and Learning: The Neglected Timing of Maturation. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science(), vol 4648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_29
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DOI: https://doi.org/10.1007/978-3-540-74913-4_29
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