Abstract
One of the serious drawbacks in Evolutionary Robotics approaches is that evolved agents in simulated environments often show significantly different behavior in real environments due to unforeseen perturbations. This is sometimes referred to as the gap problem. In order to alleviate this problem, we have so far proposed Dynamically-Rearranging Neural Networks (DRNN) by introducing the concept of neuromodulations with a diffusion-reaction mechanism of signaling molecules to so-called neuromodulators. In this study, an analysis of the evolved DRNN and a quantitative comparison with standard neural networks are presented. Through this analysis, we discuss the effect of neuromodulation on the adaptability of the evolved neurocontrollers.
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© 2001 Springer-Verlag Berlin Heidelberg
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Tokura, S., Ishiguro, A., Kawai, H., Eggenberger, P. (2001). The Effect of Neuromodulations on the Adaptability of Evolved Neurocontrollers. In: Kelemen, J., Sosík, P. (eds) Advances in Artificial Life. ECAL 2001. Lecture Notes in Computer Science(), vol 2159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44811-X_31
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DOI: https://doi.org/10.1007/3-540-44811-X_31
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